Dissertations / Theses on the topic 'Ultrasound image segmentation'
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Gong, Lixin. "Prostate ultrasound image segmentation and registration /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/5937.
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Rohlén, Robin. "Segmentation of motor units in ultrasound image sequences." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-126896.
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The archetypal modern comic book superhero, Superman, has two superpowers of interest: the ability to see into objects and the ability to see distant objects. Now, humans possess these powers as well, due to the medical ultrasound imaging and sound navigation. Ultrasound, a type of sound we cannot hear, has enabled us to see a world otherwise invisible to us. Ultrasound medical imaging can be used to visualize and quantify anatomical and functional aspects of internal tissues and organs of the human body. Skeletal muscle tissue is functionally composed by so called motor units which are the smallest voluntarily activatable units and is of primary interest in this study. The major complexity in segmentation of motor units in skeletal muscle tissue in ultrasound image sequences is the aspect of overlapping objects. We propose a framework and evaluate the performance on simulated synthetic data. We have found that it is possible to segment motor units under an isometric contraction using high-end ultrasound scanners and we have proposed a framework which is robust when simulating up to 10 components when exposed to 20 dB Gaussian white noise. The framework is not satisfactory robust when exposed to significant amount of noise. In order to be able to segment a large number of components, decomposition is inevitable and together with development of a step including smoothing, the framework can be further improved.
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Badiei, Sara. "Prostate segmentation in ultrasound images using image warping and ellipsoid fitting." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/31737.
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This thesis outlines an algorithm for 2D and 3D semi-automatic segmentation of the prostate from
B-mode trans-rectal ultrasound (TRUS) images. In semi-automatic segmentation, a computer
algorithm outlines the boundary of the prostate given a few initialization points. The algorithm
is designed for prostate brachytherapy and has the potential to: i) replace pre-operative manual
segmentation, ii) enable intra-operative segmentation, and iii) be integrated into a visualization
tool for training residents.
The segmentation algorithm makes use of image warping to make the 2D prostate boundary
elliptical. A Star Kalman based edge detector is then guided along the elliptical shape to find the
prostate boundary in the TRUS image. A second ellipse is then fit to the edge detected measurement
points. Once all 2D slices are segmented in this manner an ellipsoid is fit to the 3D cloud of points.
Finally a reverse warping algorithm gives us the segmented prostate volume.
In-depth 2D and 3D clinical studies show promising results. In 2D, distance based metrics show
a mean absolute difference of 0.67 ± 0.18mm between manual and semi-automatic segmentation
and area based metrics show average sensitivity and accuracy over 97% and 93% respectively. In
3D, i) the difference between manual and semi-automatic segmentation is on the order of interobserver
variability, ii) the repeatability of the segmentation algorithm is consistently better than
the intra-observer variability, and iii) the sensitivity and accuracy are 97% and 85% respectively.
The 3D algorithm requires only 5 initialization points and can segment a prostate volume in less
than 10 seconds (approximately 40 times faster than manual segmentation).
The novelties of this algorithm, in comparison to other works, are in the warping and ellipse/
ellipsoid fitting steps. These two combine to provide a simple solution that works well even
with non-ideal images to produce accurate, real-time results.Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Quartararo, John David. "Semi-Automated Segmentation of 3D Medical Ultrasound Images." Digital WPI, 2009. https://digitalcommons.wpi.edu/etd-theses/155.
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A level set-based segmentation procedure has been implemented to identify target object boundaries from 3D medical ultrasound images. Several test images (simulated, scanned phantoms, clinical) were subjected to various preprocessing methods and segmented. Two metrics of segmentation accuracy were used to compare the segmentation results to ground truth models and determine which preprocessing methods resulted in the best segmentations. It was found that by using an anisotropic diffusion filtering method to reduce speckle type noise with a 3D active contour segmentation routine using the level set method resulted in semi-automated segmentation on par with medical doctors hand-outlining the same images.
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Ghose, Soumya. "Robust image segmentation applied to magnetic resonance and ultrasound images of the prostate." Doctoral thesis, Universitat de Girona, 2012. http://hdl.handle.net/10803/98524.
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Prostate segmentation in trans rectal ultrasound (TRUS) and magnetic resonance images (MRI) facilitates volume estimation, multi-modal image registration, surgical planing and image guided prostate biopsies. The objective of this thesis is to develop computationally efficient prostate segmentation algorithms in both TRUS and MRI image modalities. In this thesis we propose a probabilistic learning approach to achieve a soft classification of the prostate for automatic initialization and evolution of a deformable model for prostate segmentation. Two deformable models are developed for the TRUS segmentation. An explicit shape and region prior based deformable model and an implicit deformable model guided by an energy minimization framework. Besides, in MRI, the posterior probabilities are fused with the soft segmentation coming from an atlas segmentation and a graph cut based energy minimization achieves the final segmentation. In both image modalities, statistically significant improvement are achieved compared to current works in the literature.La segmentació de la pròstata en imatge d'ultrasò (US) i de ressonància magnètica (MRI) permet l'estimació del volum, el registre multi-modal i la planificació quirúrgica de biòpsies guiades per imatge. L'objectiu d'aquesta tesi és el desenvolupament d'algorismes automàtics per a la segmentació de la pròstata en aquestes modalitats. Es proposa un aprenentatge automàtic inical per obtenir una primera classificació de la pròstata que permet, a continuació, la inicialització i evolució de diferents models deformables. Per imatges d'US, es proposen un model explícit basat en forma i informació regional i un model implícit basat en la minimització d'una funció d'energia. En MRI, les probalitats inicials es fusionen amb una imatge de probabilitat provinent d'una segmentació basada en atlas, i la minimització es realitza mitjançant tècniques de grafs. El resultat final és una significant millora dels algorismes actuals en ambdues modalitats d'imatge.
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Wen, Shuangyue. "Automatic Tongue Contour Segmentation using Deep Learning." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38343.
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Ultrasound is one of the primary technologies used for clinical purposes. Ultrasound systems have favorable real-time capabilities, are fast and relatively inexpensive, portable and non-invasive. Recent interest in using ultrasound imaging for tongue motion has various applications in linguistic study, speech therapy as well as in foreign language education, where visual-feedback of tongue motion complements conventional audio feedback.
Ultrasound images are known to be difficult to recognize. The anatomical structure in them, the rapidity of tongue movements, also missing segments in some frames and the limited frame rate of ultrasound systems have made automatic tongue contour extraction and tracking very challenging and especially hard for real-time applications. Traditional image processing-based approaches have many practical limitations in terms of automation, speed, and accuracy.
Recent progress in deep convolutional neural networks has been successfully exploited in a variety of computer vision problems such as detection, classification, and segmentation. In the past few years, deep belief networks for tongue segmentation and convolutional neural networks for the classification of tongue motion have been proposed. However, none of these claim fully-automatic or real-time performance. U-Net is one of the most popular deep learning algorithms for image segmentation, and it is composed of several convolutions and deconvolution layers.
In this thesis, we proposed a fully automatic system to extract tongue dorsum from ultrasound videos in real-time using a simplified version of U-Net, which we call sU-Net. Two databases from different machines were collected, and different training schemes were applied for testing the learning capability of the model. Our experiment on ultrasound video data demonstrates that the proposed method is very competitive compared with other methods in terms of performance and accuracy.
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Zhao, Ningning. "Inverse problems in medical ultrasound images - applications to image deconvolution, segmentation and super-resolution." Phd thesis, Toulouse, INPT, 2016. http://oatao.univ-toulouse.fr/16613/1/Zhao.pdf.
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In the field of medical image analysis, ultrasound is a core imaging modality employed due to its real time and easy-to-use nature, its non-ionizing and low cost characteristics. Ultrasound imaging is used in numerous clinical applications, such as fetus monitoring, diagnosis of cardiac diseases, flow estimation, etc. Classical applications in ultrasound imaging involve tissue characterization, tissue motion estimation or image quality enhancement (contrast, resolution, signal to noise ratio). However, one of the major problems with ultrasound images, is the presence of noise, having the form of a granular pattern, called speckle. The speckle noise in ultrasound images leads to the relative poor image qualities compared with other medical image modalities, which limits the applications of medical ultrasound imaging. In order to better understand and analyze ultrasound images, several device-based techniques have been developed during last 20 years. The object of this PhD thesis is to propose new image processing methods allowing us to improve ultrasound image quality using postprocessing techniques. First, we propose a Bayesian method for joint deconvolution and segmentation of ultrasound images based on their tight relationship. The problem is formulated as an inverse problem that is solved within a Bayesian framework. Due to the intractability of the posterior distribution associated with the proposed Bayesian model, we investigate a Markov chain Monte Carlo (MCMC) technique which generates samples distributed according to the posterior and use these samples to build estimators of the ultrasound image. In a second step, we propose a fast single image super-resolution framework using a new analytical solution to the l2-l2 problems (i.e., $\ell_2$-norm regularized quadratic problems), which is applicable for both medical ultrasound images and piecewise/ natural images. In a third step, blind deconvolution of ultrasound images is studied by considering the following two strategies: i) A Gaussian prior for the PSF is proposed in a Bayesian framework. ii) An alternating optimization method is explored for blind deconvolution of ultrasound.
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von, Lavante Etienne. "Segmentation and sizing of breast cancer masses with ultrasound elasticity imaging." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:81225f61-6b83-405b-aed5-17b316ed586a.
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Uncertainty in the sizing of breast cancer masses is a major issue in breast screening programs, as there is a tendency to severely underestimate the sizing of malignant masses, especially with ultrasound imaging as part of the standard triple assessment. Due to this issue about 20% of all surgically treated women have to undergo a second resection, therefore the aim of this thesis is to address this issue by developing novel image analysis methods. Ultrasound elasticity imaging has been proven to have a better ability to differentiate soft tissues compared to standard B-mode. Thus a novel segmentation algorithm is presented, employing elasticity imaging to improve the sizing of malignant breast masses in ultrasound. The main contributions of this work are the introduction of a novel filtering technique to significantly improve the quality of the B-mode image, the development of a segmentation algorithm and their application to an ongoing clinical trial. Due to the limitations of the employed ultrasound device, the development of a method to improve the contrast and signal to noise ratio of B-mode images was required. Thus, an autoregressive model based filter on the radio-frequency signal is presented which is able to reduce the misclassification error on a phantom by up to 90% compared to the employed device, achieving similar results to a state-of-the art ultrasound system. By combining the output of this filter with elasticity data into a region based segmentation framework, a computationally highly efficient segmentation algorithm using Graph-cuts is presented. This method is shown to successfully and reliably segment objects on which previous highly cited methods have failed. Employing this method on 18 cases from a clinical trial, it is shown that the mean absolute error is reduced by 2 mm, and the bias of the B-Mode sizing to underestimate the size was overcome. Furthermore, the ability to detect widespread DCIS is demonstrated.
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Rackham, Thomas. "Ultrasound segmentation tools and their application to assess fetal nutritional health." Thesis, University of Oxford, 2016. http://ora.ox.ac.uk/objects/uuid:5d102b18-dd32-4004-8aa5-b04242139daa.
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Maternal diet can have a great impact on the health and development of the fetus. Poor fetal nutrition has been linked to the development of a set of conditions in later life, such as coronary heart disease, type 2 diabetes and hypertension, while restricted growth can result in hypogylcemia, hypocalcemia, hypothermia, polycythemia, hyperbilirubinemia and cerebral palsy. High alcohol consumption during pregnancy can result in Fetal Alcohol Syndrome, a condition that can cause growth retardation, lowered intelligence and craniofacial defects. Current biometric assessment of the fetus involves size-based measures which may not accurately portray the state of fetal development, since they cannot differentiate cases of small-but-healthy or large-but-unhealthy fetuses. This thesis aims to outline a set of more appropriate measures of accurately capturing the state of fetal development. Specifically, soft tissue area and liver volume measurement are examined, followed by facial shape characterisation. A number of tools are presented which aim to allow clinicians to achieve accurate segmentations of these landmark regions. These are modifications on the Live Wire algorithm, an interactive segmentation method in which the user places a number of anchor points and a minimum cost path is calculated between the previous anchor point and the cursor. This focuses on giving the clinician intuitive control over the exact position of the segmented contour. These modifications are FA-S Live Wire, which utilises Feature Asymmetry and a weak shape constraint, ASP Live Wire, which is a 3D expansion of Live Wire, and FA-O Live Wire, which uses Feature Asymmtery and Local Orientation to guide the segmentation process. These have been designed with each of the specific biometric landmarks in mind. Finally, a method of characterising fetal face shape is proposed, using a combination of the segmentation methods described here and a simple shape model with a parameterised b-spline meshing approach to facial surface representation.
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Navarrete, Hurtado Hugo Ariel. "Electromagnetic models for ultrasound image processing." Doctoral thesis, Universitat Politècnica de Catalunya, 2016. http://hdl.handle.net/10803/398235.
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Speckle noise appears when coherent illumination is employed, as for example Laser, Synthetic Aperture Radar (SAR), Sonar, Magnetic Resonance, X-ray and Ultrasound imagery. Backscattered echoes from the randomly distributed scatterers in the microscopic structure of the medium are the origin of speckle phenomenon, which characterizes coherent imaging with a granular appearance. It can be shown that speckle noise is of multiplicative nature, strongly correlated and more importantly, with non-Gaussian statistics. These characteristics differ greatly from the traditional assumption of white additive Gaussian noise, often taken in image segmentation, filtering, and in general, image processing; which leads to reduction of the methods effectiveness for final image information extraction; therefore, this kind of noise severely impairs human and machine ability to image interpretation.
Statistical modeling is of particular relevance when dealing with speckled data in order to obtain efficient image processing algorithms; but, additionally, clinical ultrasound imaging systems employ nonlinear signal processing to reduce the dynamic range of the input echo signal to match the smaller dynamic range of the display device and to emphasize objects with weak backscatter. This reduction in dynamic range is normally achieved through a logarithmic amplifier i.e. logarithmic compression, which selectively compresses large input signals. This kind of nonlinear compression totally changes the statistics of the input envelope signal; and, a closed form expression for the density function of the logarithmic transformed data is usually hard to derive.
This thesis is concerned with the statistical distributions of the Log-compressed amplitude signal in coherent imagery, and its main objective is to develop a general statistical model for log-compressed ultrasound B-scan images. The developed model is adapted, making the pertinent physical analogies, from the multiplicative model in Synthetic Aperture Radar (SAR) context. It is shown that the proposed model can successfully describe log-compressed data generated from different models proposed in the specialized ultrasound image processing literature. Also, the model is successfully applied to model in-vivo echo-cardiographic (ultrasound) B-scan images. Necessary theorems are established to account for a rigorous mathematical proof of the validity and generality of the model. Additionally, a physical interpretation of the parameters is given, and the connections between the generalized central limit theorems, the multiplicative model and the compound representations approaches for the different models proposed up-to-date, are established. It is shown that the log-amplifier parameters are included as model parameters and all the model parameters are estimated using moments and maximum likelihood methods. Finally, three applications are developed: speckle noise identification and filtering; segmentation of in vivo echo-cardiographic (ultrasound) B-scan images and a novel approach for heart ejection fraction evaluationEl ruido Speckle aparece cuando se utilizan sistemas de iluminación coherente, como por ejemplo Láser, Radar de Apertura Sintética (SAR), Sonar, Resonancia Magnética, rayos X y ultrasonidos. Los ecos dispersados por los centros dispersores distribuidos al azar en la estructura microscópica del medio son el origen de este fenómeno, que caracteriza las imágenes coherentes con un aspecto granular. Se puede demostrar que el ruido Speckle es de carácter multiplicativo, fuertemente correlacionados y lo más importante, con estadística no Gaussiana. Estas características son muy diferentes de la suposición tradicional de ruido aditivo gaussiano blanco, a menudo asumida en la segmentación de imágenes, filtrado, y en general, en el procesamiento de imágenes; lo cual se traduce en la reducción de la eficacia de los métodos para la extracción de información de la imagen final. La modelización estadística es de particular relevancia cuando se trata con datos Speckle, a fin de obtener algoritmos de procesamiento de imágenes eficientes. Además, el procesamiento no lineal de señales empleado en sistemas clínicos de imágenes por ultrasonido para reducir el rango dinámico de la señal de eco de entrada de manera que coincida con el rango dinámico más pequeño del dispositivo de visualización y resaltar así los objetos con dispersión más débil, modifica radicalmente la estadística de los datos. Esta reducción en el rango dinámico se logra normalmente a través de un amplificador logarítmico es decir, la compresión logarítmica, que comprime selectivamente las señales de entrada y una forma analítica para la expresión de la función de densidad de los datos transformados logarítmicamente es por lo general difícil de derivar. Esta tesis se centra en las distribuciones estadísticas de la amplitud de la señal comprimida logarítmicamente en las imágenes coherentes, y su principal objetivo es el desarrollo de un modelo estadístico general para las imágenes por ultrasonido comprimidas logarítmicamente en modo-B. El modelo desarrollado se adaptó, realizando las analogías físicas relevantes, del modelo multiplicativo en radares de apertura sintética (SAR). El Modelo propuesto puede describir correctamente los datos comprimidos logarítmicamente a partir datos generados con los diferentes modelos propuestos en la literatura especializada en procesamiento de imágenes por ultrasonido. Además, el modelo se aplica con éxito para modelar ecocardiografías en vivo. Se enuncian y demuestran los teoremas necesarios para dar cuenta de una demostración matemática rigurosa de la validez y generalidad del modelo. Además, se da una interpretación física de los parámetros y se establecen las conexiones entre el teorema central del límite generalizado, el modelo multiplicativo y la composición de distribuciones para los diferentes modelos propuestos hasta a la fecha. Se demuestra además que los parámetros del amplificador logarítmico se incluyen dentro de los parámetros del modelo y se estiman usando los métodos estándar de momentos y máxima verosimilitud. Por último, tres aplicaciones se desarrollan: filtrado de ruido Speckle, segmentación de ecocardiografías y un nuevo enfoque para la evaluación de la fracción de eyección cardiaca.
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Hammer, Steven James. "Engineering a 3D ultrasound system for image-guided vascular modelling." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/4253.
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Atherosclerosis is often diagnosed using an ultrasound (US) examination in the carotid and femoral arteries and the abdominal aorta. A decision to operate requires two measures of disease severity: the degree of stenosis measured using B-mode US; and the blood flow patterns in the artery measured using spectral Doppler US. However other biomechanical factors such as wall shear stress (WSS) and areas of flow recirculation are also important in disease development and rupture. These are estimated using an image-guided modelling approach, where a three-dimensional computational mesh of the artery is simulated. To generate a patient-specific arterial 3D computational mesh, a 3D ultrasound (3DUS) system was developed. This system uses a standard clinical US scanner with an optical position sensor to measure the position of the transducer; a video capture card to record video images from the scanner; and a PC running Stradwin software to reconstruct 3DUS data. The system was characterised using an industry-standard set of calibration phantoms, giving a reconstruction accuracy of ± 0.17 mm with a 12MHz linear array transducer. Artery movements from pulsatile flow were reduced using a retrospective gating technique. The effect of pressure applied to the transducer moving and deforming the artery was reduced using an image-based rigid registration technique. The artery lumen found on each 3DUS image was segmented using a semi-automatic segmentation technique known as ShIRT (the Sheffield Image Registration Toolkit). Arterial scans from healthy volunteers and patients with diagnosed arterial disease were segmented using the technique. The accuracy of the semi-automatic technique was assessed by comparing it to manual segmentation of each artery using a set of segmentation metrics. The mean accuracy of the semi-automatic technique ranged from 85% to 99% and depended on the quality of the images and the complexity of the shape of the lumen. Patient-specific 3D computational artery meshes were created using ShIRT. An idealised mesh was created using key features of the segmented 3DUS scan. This was registered and deformed to the rest of the segmented dataset, producing a mesh that represents the shape of the artery. Meshes created using ShIRT were compared to meshes created using the Rhino solid modelling package. ShIRT produced smoother meshes; Rhino reproduced the shape of arterial disease more accurately. The use of 3DUS with image-guided modelling has the potential to be an effective tool in the diagnosis of atherosclerosis. Simulations using these data reflect in vivo studies of wall shear stress and recirculation in diseased arteries and are comparable with results in the literature created using MRI and other 3DUS systems.
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AbuAzzah, Ezzat. "A 4D ultrasound imaging automation platform for modelling and assessment of ultrasound target dynamics using direct visual servoing and machine learning." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/134613/1/Ezzat_AbuAzzah_Thesis.pdf.
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Purpose: The main objective of this research project is to address the limitations of image-processing computational performance of current ultrasound target motion estimation techniques by developing a novel high-performance ultrasound motion estimation technique that eliminates the need for using currently adopted image-registration-based motion-estimation techniques for systems requiring ultrasound target tracking. To address the system's dependence on the operator's experience and supervision as the second objective of this research project, the developed target tracking technique is integrated with a novel automation platform developed to support medical physicists with tools for collectively implementing, supervising, and training arbitrary ultrasound target motion estimation tasks and incorporate them in the platform as reusable solutions to support qualified and reproducible research.
Methodologies: The main objective of this research project has been addressed by enabling high-performance and accurate motion estimation of soft-tissue-equivalent ultrasound targets using the ultrasound direct visual servoing (US-DVS) technique for the first time. The inaccurate US-target tracking capabilities of the implemented US-DVS technique have been addressed and optimised using specially trained machine-learning models which lead to the US direct visual modelling (US-DVM) technique introduced by this project. The machine-learning models implemented by the US-DVM technique were trained using two types of US-target motion simulations: simulation of tissue-equivalent US-target motion based on predefined motion trajectories provided by high-precision robotic-arms; and simulations using an ultrasound digital-target dynamics simulator (US-DTDS) developed by this project. The second objective of this research project has been addressed by maximising the performance and target detection accuracy of the proposed motion estimation technique by minimising its dependence on the operator and by transferring the operator's target scanning and identification experience to a provenance-enabled automation system. To Achieve this, another machine-learning model, based on Gaussian mixture modelling (GMM), was also developed and used to improve the performance of standard image segmentation techniques allowing for automating target detection and tracking in real-time. In addition, the interaction of the operator with the platform has been optimised by employing a provenance-enabled workflow automation framework, called VisTrails, to implement the proposed new technique and all the supporting services required. This will help in learning the operator's skills by the automation workflow, which will minimise the operators' systematic errors and support reproducible research.
Results: A Medical Physics Services Framework (MPS-F) has been developed based on VisTrails and used to control two robotic arms to track and capture the simulated motion using the US-DVM technique. The new US-DVM technique has enabled accurate estimation of ultrasound target motion from US-DVS tracking feedback with accuracies better than 1.5%, and with computational performance up to 14 times better than motion estimation techniques used in current practice. This allows for more accurate real-time tracking of ultrasound targets like the prostate. Regarding the second objective, high-performance detection and extraction of deformable ultrasound targets using the morphological active-contour technique (MACT) has been achieved for average prostate-size targets to within 0.04 seconds using down-sampling strategies. To further support the second objective, the MPS-F has been developed as a provenance-enabled software automation solution to enable other researchers to reuse and reproduce most of the implementations and results of this research, and hence minimise the operator's systematic errors. The MPS-F has been used to implement a novel technique that combined the MACT and the US-DVM to detect and estimate prostate-size target deformations with volume accuracies better than 0.005 cm^3.
Conclusion: This research study introduced the US-DVM technique as a novel ultrasound target motion modelling and estimation solution based on ultrasound direct visual servoing (US-DVS). US-DVS is used typically for ultrasound target tracking in real-time and using it for modelling and estimating target dynamics has been addressed for the first time by this study based on machine learning strategies. The machine learning approach provided solutions that overcame inherent limitations of the imaging system allowing for predicting faster dynamics and larger interframe displacements, in addition to enabling supervised and automated predictions of accurate motion estimations tailored specifically for individual targets for optimal consistency and accuracy.
The proposed techniques and solutions have been implemented and evaluated by the medical physics services framework (MPS-F), which was developed by this project based on the VisTrails workflow management system. The MPS-F is considered a major contribution by this study where it enables the operator to implement, train, and supervise arbitrary medical physics workflows that can employ machine-learning, image-processing, and ultrasound target tracking and motion estimation tasks. With the help of the MPF-S, it is proposed by this study that an ultrasound modelling and automation platform (US-MAP) can be constructed as a more efficient real-time alternative for the Elekta Clarity Autoscan system being the only non-telerobotic real-time ultrasound target tracking system implemented clinically for tracking prostate motion.
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Chiu, Bernard. "A new segmentation algorithm for prostate boundary detection in 2D ultrasound images." Thesis, Waterloo, Ont. : University of Waterloo, [Dept. of Electrical and Computer Engineering], 2003. http://etd.uwaterloo.ca/etd/bcychiu2003.pdf.
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Thesis (M.Sc.)--University of Waterloo, 2003."A thesis presented to the University of Waterloo in fulfilment of the thesis requirement for the degree of Master of Applied Science in Electrical and Computer Engineering". Includes bibliographical references.
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Yaqub, Mohammad. "Automatic measurements of femoral characteristics using 3D ultrasound images in utero." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:857a12d2-ffe3-4fa6-89c3-0d8319ee2fbb.
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Vitamin D is very important for endochondral ossification and it is commonly insufficient during pregnancy (Javaid et al., 2006). Insufficiency of vitamin D during pregnancy predicts bone mass and hence predicts adult osteoporosis (Javaid et al., 2006). The relationship between maternal vitamin D and manually measured fetal biometry has been studied (Mahon et al., 2009). However, manual fetal biometry especially volumetric measurements are subjective, time-consuming and possibly irreproducible. Computerised measurements can overcome or at least reduce such problems. This thesis concerns the development and evaluation of novel methods to do this. This thesis makes three contributions. Firstly, we have developed a novel technique based on the Random Forests (RF) classifier to segment and measure several fetal femoral characteristics from 3D ultrasound volumes automatically. We propose a feature selection step in the training stage to eliminate irrelevant features and utilise the "good" ones. We also develop a weighted voting mechanism to weight tree probabilistic decisions in the RF classifier. We show that the new RF classifier is more accurate than the classic method (Yaqub et al., 2010b, Yaqub et al., 2011b). We achieved 83% segmentation precision using the proposed technique compared to manually segmented volumes. The proposed segmentation technique was also validated on segmenting adult brain structures in MR images and it showed excellent accuracy. The second contribution is a wavelet-based image fusion technique to enhance the quality of the fetal femur and to compensate for missing information in one volume due to signal attenuation and acoustic shadowing. We show that using image fusion to increase the image quality of ultrasound images of bony structures leads to a more accurate and reproducible assessment and measurement qualitatively and quantitatively (Yaqub et al., 2010a, Yaqub et al., 2011a). The third contribution concerns the analysis of data from a cohort study of 450 fetal femoral ultrasound volumes (18-21 week gestation). The femur length, cross-sectional areas, volume, splaying indices and angles were automatically measured using the RF method. The relationship between these measurements and the fetal gestational age and maternal vitamin D was investigated. Segmentation of a fetal femur is fast (2.3s/volume), thanks to the parallel implementation. The femur volume, length, splaying index were found to significantly correlate with fetal gestational age. Furthermore, significant correlations between the automatic measurements and 10 nmol increment in maternal 25OHD during second trimester were found.
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Stebbing, Richard. "Model-based segmentation methods for analysis of 2D and 3D ultrasound images and sequences." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:f0e855ca-5ed9-4e40-994c-9b470d5594bf.
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This thesis describes extensions to 2D and 3D model-based segmentation algorithms for the analysis of ultrasound images and sequences. Starting from a common 2D+t "track-to-last" algorithm, it is shown that the typical method of searching for boundary candidates perpendicular to the model contour is unnecessary if, for each boundary candidate, its corresponding position on the model contour is optimised jointly with the model contour geometry. With this observation, two 2D+t segmentation algorithms, which accurately recover boundary displacements and are capable of segmenting arbitrarily long sequences, are formulated and validated. Generalising to 3D, subdivision surfaces are shown to be natural choices for continuous model surfaces, and the algorithms necessary for joint optimisation of the correspondences and model surface geometry are described. Three applications of 3D model-based segmentation for ultrasound image analysis are subsequently presented and assessed: skull segmentation for fetal brain image analysis; face segmentation for shape analysis, and single-frame left ventricle (LV) segmentation from echocardiography images for volume measurement. A framework to perform model-based segmentation of multiple 3D sequences - while jointly optimising an underlying linear basis shape model - is subsequently presented for the challenging application of right ventricle (RV) segmentation from 3D+t echocardiography sequences. Finally, an algorithm to automatically select boundary candidates independent of a model surface estimate is described and presented for the task of LV segmentation. Although motivated by challenges in ultrasound image analysis, the conceptual contributions of this thesis are general and applicable to model-based segmentation problems in many domains. Moreover, the components are modular, enabling straightforward construction of application-specific formulations for new clinical problems as they arise in the future.
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Sun, Shanhui. "Automated and interactive approaches for optimal surface finding based segmentation of medical image data." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/3543.
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Optimal surface finding (OSF), a graph-based optimization approach to image segmentation, represents a powerful framework for medical image segmentation and analysis. In many applications, a pre-segmentation is required to enable OSF graph construction. Also, the cost function design is critical for the success of OSF. In this thesis, two issues in the context of OSF segmentation are addressed. First, a robust model-based segmentation method suitable for OSF initialization is introduced. Second, an OSF-based segmentation refinement approach is presented.
For segmenting complex anatomical structures (e.g., lungs), a rough initial segmentation is required to apply an OSF-based approach. For this purpose, a novel robust active shape model (RASM) is presented. The RASM matching in combination with OSF is investigated in the context of segmenting lungs with large lung cancer masses in 3D CT scans. The robustness and effectiveness of this approach is demonstrated on 30 lung scans containing 20 normal lungs and 40 diseased lungs where conventional segmentation methods frequently fail to deliver usable results. The developed RASM approach is generally applicable and suitable for large organs/structures.
While providing high levels of performance in most cases, OSF-based approaches may fail in a local region in the presence of pathology or other local challenges. A new (generic) interactive refinement approach for correcting local segmentation errors based on the OSF segmentation framework is proposed. Following the automated segmentation, the user can inspect the result and correct local or regional segmentation inaccuracies by (iteratively) providing clues regarding the location of the correct surface. This expert information is utilized to modify the previously calculated cost function, locally re-optimizing the underlying modified graph without a need to start the new optimization from scratch. For refinement, a hybrid desktop/virtual reality user interface based on stereoscopic visualization technology and advanced interaction techniques is utilized for efficient interaction with the segmentations (surfaces). The proposed generic interactive refinement method is adapted to three applications. First, two refinement tools for 3D lung segmentation are proposed, and the performance is assessed on 30 test cases from 18 CT lung scans. Second, in a feasibility study, the approach is expanded to 4D OSF-based lung segmentation refinement and an assessment of performance is provided. Finally, a dual-surface OSF-based intravascular ultrasound (IVUS) image segmentation framework is introduced, application specific segmentation refinement methods are developed, and an evaluation on 41 test cases is presented. As demonstrated by experiments, OSF-based segmentation refinement is a promising approach to address challenges in medical image segmentation.
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Kasimoglu, Ismail Hakki. "Estimation of a Coronary Vessel Wall Deformation with High-Frequency Ultrasound Elastography." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19762.
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Elastography, which is based on applying pressure and estimating the resulting deformation, involves the forward problem to obtain the strain distributions and inverse problem to construct the elastic distributions consistent with the obtained strains on observation points. This thesis focuses on the former problem whose solution is used as an input to the latter problem. The aim is to provide the inverse problem community with accurate strain estimates of a coronary artery vessel wall. In doing so, a new ultrasonic image-based elastography approach is developed. Because the accuracy and quality of the estimated strain fields depend on the resolution level of the ultrasound image and to date best resolution levels obtained in the literature are not enough to clearly see all boundaries of the artery, one of the main goals is to acquire high-resolution coronary vessel wall ultrasound images at different pressures. For this purpose, first an experimental setup is designed to collect radio frequency (RF) signals, and then image formation algorithm is developed to obtain ultrasound images from the collected signals. To segment the noisy ultrasound images formed, a geodesic active contour-based segmentation algorithm with a novel stopping function that includes local phase of the image is developed. Then, region-based information is added to make the segmentation more robust to noise. Finally, elliptical deformable template is applied so that a priori information regarding the shape of the arteries could be taken into account, resulting in more stable and accurate results. The use of this template also implicitly provides boundary point correspondences from which high-resolution, size-independent, non-rigid and local strain fields of the coronary vessel wall are obtained.
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Mozaffari, Maaref Mohammad Hamed. "A Real-Time and Automatic Ultrasound-Enhanced Multimodal Second Language Training System: A Deep Learning Approach." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40477.
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The critical role of language pronunciation in communicative competence is significant, especially for second language learners. Despite renewed awareness of the importance of articulation, it remains a challenge for instructors to handle the pronunciation needs of language learners. There are relatively scarce pedagogical tools for pronunciation teaching and learning, such as inefficient, traditional pronunciation instructions like listening and repeating. Recently, electronic visual feedback (EVF) systems (e.g., medical ultrasound imaging) have been exploited in new approaches in such a way that they could be effectively incorporated in a range of teaching and learning contexts. Evaluation of ultrasound-enhanced methods for pronunciation training, such as multimodal methods, has asserted that visualizing articulator’s system as biofeedback to language learners might improve the efficiency of articulation learning. Despite the recent successful usage of multimodal techniques for pronunciation training, manual works and human manipulation are inevitable in many stages of those systems. Furthermore, recognizing tongue shape in noisy and low-contrast ultrasound images is a challenging job, especially for non-expert users in real-time applications. On the other hand, our user study revealed that users could not perceive the placement of their tongue inside the mouth comfortably just by watching pre-recorded videos.
Machine learning is a subset of Artificial Intelligence (AI), where machines can learn by experiencing and acquiring skills without human involvement. Inspired by the functionality of the human brain, deep artificial neural networks learn from large amounts of data to perform a task repeatedly. Deep learning-based methods in many computer vision tasks have emerged as the dominant paradigm in recent years. Deep learning methods are powerful in automatic learning of a new job, while unlike traditional image processing methods, they are capable of dealing with many challenges such as object occlusion, transformation variant, and background artifacts. In this dissertation, we implemented a guided language pronunciation training system, benefits from the strengths of deep learning techniques. Our modular system attempts to provide a fully automatic and real-time language pronunciation training tool using ultrasound-enhanced augmented reality. Qualitatively and quantitatively assessments indicate an exceptional performance for our system in terms of flexibility, generalization, robustness, and autonomy outperformed previous techniques. Using our ultrasound-enhanced system, a language learner can observe her/his tongue movements during real-time speech, superimposed on her/his face automatically.
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Eklund, Anders, Paul Dufort, Daniel Forsberg, and Stephen LaConte. "Medical Image Processing on the GPU : Past, Present and Future." Linköpings universitet, Medicinsk informatik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-93673.
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Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.
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Prevost, Raphaël. "Méthodes variationnelles pour la segmentation d'images à partir de modèles : applications en imagerie médicale." Phd thesis, Université Paris Dauphine - Paris IX, 2013. http://tel.archives-ouvertes.fr/tel-00932995.
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La segmentation d'images médicales est depuis longtemps un sujet de recherche actif. Cette thèse traite des méthodes de segmentation basées modèles, qui sont un bon compromis entre généricité et capacité d'utilisation d'informations a priori sur l'organe cible. Notre but est de construire un algorithme de segmentation pouvant tirer profit d'une grande variété d'informations extérieures telles que des bases de données annotées (via l'apprentissage statistique), d'autres images du même patient (via la co-segmentation) et des interactions de l'utilisateur. Ce travail est basé sur la déformation de modèle implicite, une méthode variationnelle reposant sur une représentation implicite des formes. Après avoir amélioré sa formulation mathématique, nous montrons son potentiel sur des problèmes cliniques difficiles. Nous introduisons ensuite différentes généralisations, indépendantes mais complémentaires, visant à enrichir le modèle de forme et d'apparence utilisé. La diversité des applications cliniques traitées prouve la généricité et l'efficacité de nos contributions.
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Leclerc, Sarah Marie-Solveig. "Automatisation de la segmentation sémantique de structures cardiaques en imagerie ultrasonore par apprentissage supervisé." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI121.
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L’analyse d’images médicales joue un rôle essentiel en cardiologie pour la réalisation du diagnostique cardiaque clinique et le suivi de l’état du patient. Parmi les modalités d’imagerie utilisées, l’imagerie par ultrasons, temps réelle, moins coûteuse et portable au chevet du patient, est de nos jours la plus courante. Malheureusement, l’étape nécessaire de segmentation sémantique (soit l’identification et la délimitation précise) des structures cardiaques est difficile en échocardiographie à cause de la faible qualité des images ultrasonores, caractérisées en particulier par l’absence d’interfaces nettes entre les différents tissus. Pour combler le manque d’information, les méthodes les plus performante, avant ces travaux, reposaient sur l’intégration d’informations a priori sur la forme ou le mouvement du cœur, ce qui en échange réduisait leur adaptabilité au cas par cas. De plus, de telles approches nécessitent pour être efficaces l’identification manuelle de plusieurs repères dans l’image, ce qui rend le processus de segmentation difficilement reproductible. Dans cette thèse, nous proposons plusieurs algorithmes originaux et entièrement automatiques pour la segmentation sémantique d’images échocardiographiques. Ces méthodes génériques sont adaptées à la segmentation échocardiographique par apprentissage supervisé, c’est-à-dire que la résolution du problème est construite automatiquement à partir de données pré- analysées par des cardiologues entraînés. Grâce au développement d’une base de données et d’une plateforme d’évaluation dédiées au projet, nous montrons le fort potentiel clinique des méthodes automatiques d’apprentissage supervisé, et en particulier d’apprentissage profond, ainsi que la possibilité d’améliorer leur robustesse en intégrant une étape de détection automatique des régions d’intérêt dans l’imageThe analysis of medical images plays a critical role in cardiology. Ultrasound imaging, as a real-time, low cost and bed side applicable modality, is nowadays the most commonly used image modality to monitor patient status and perform clinical cardiac diagnosis. However, the semantic segmentation (i.e the accurate delineation and identification) of heart structures is a difficult task due to the low quality of ultrasound images, characterized in particular by the lack of clear boundaries. To compensate for missing information, the best performing methods before this thesis relied on the integration of prior information on cardiac shape or motion, which in turns reduced the adaptability of the corresponding methods. Furthermore, such approaches require man- ual identifications of key points to be adapted to a given image, which makes the full process difficult to reproduce. In this thesis, we propose several original fully-automatic algorithms for the semantic segmentation of echocardiographic images based on supervised learning ap- proaches, where the resolution of the problem is automatically set up using data previously analyzed by trained cardiologists. From the design of a dedicated dataset and evaluation platform, we prove in this project the clinical applicability of fully-automatic supervised learning methods, in particular deep learning methods, as well as the possibility to improve the robustness by incorporating in the full process the prior automatic detection of regions of interest
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Li, Yingping. "Artificial intelligence and radiomics in cancer diagnosis." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG053.
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L'intelligence artificielle (IA) est très utilisée pour le diagnostic et le traitement de données médicales, donnant lieu à la médecine personnalisée assistée par l'IA. Ce manuscrit se concentre sur la proposition et l'analyse de méthodes d'IA, notamment l'apprentissage profond et la radiomique, pour le diagnostic du cancer. Tout d'abord, une approche efficace de segmentation automatique est essentielle pour mettre en place une méthode de diagnostic par IA, car c'est un préalable à une analyse par radiomiques. Nous avons proposé une nouvelle approche pour la segmentation automatique des lésions dans les images échographiques, basée sur des données multicentrique et multipathologique présentant différents types de cancers. En introduisant la convolution de groupe, nous avons proposé un réseau U-net léger en mémoire sans sacrifier les performances de segmentation. Deuxièmement, nous nous sommes intéressés au traitement de données d'imagerie par résonance magnétique (IRM) pour prédire de manière non invasive le sous-type de gliome, défini par le grade de la tumeur, la mutation de l'isocitrate déshydrogénase (IDH) et la codélétion 1p/19q. Nous proposons une approche par radiomiques. La performance de prédiction s'est améliorée de manière significative en optimisant différents paramètres de notre modèle. Les caractéristiques des éléments radiomiques qui distinguent le mieux les sous-types de gliome ont également été analysées. Ce travail a non seulement fourni un pipeline qui fonctionne bien pour prédire le sous-type de gliome, mais il a également contribué au développement et à l'interprétabilité du modèle radiomique. Troisièmement, nous nous intéressons à la problématique de reproductibilité des approches basées sur les radiomiques. Nous avons donc étudié l'impact de différentes méthodes de prétraitement d'images et de méthodes d'harmonisation (y compris la normalisation de l'intensité et l'harmonisation ComBat) sur la reproductibilité des caractéristiques radiomiques en IRM. Nous avons montré que la méthode ComBat est essentielle pour éliminer la variation non biologique causée par les différents paramètres d'acquisition d'image (à savoir, les effets du scanner) et améliorer la reproductibilité des caractéristiques dans les études radiomiques. Nous avons illustré l'importance de la normalisation de l'intensité, car elle permet d'obtenir des images IRM plus comparables et des résultats plus robustes. Enfin, nous avons cherché à améliorer la méthode ComBat en modifiant l'hypothèse classique, à savoir que les effets du scanner sont différents pour différentes classes (comme les tumeurs et les tissus normaux). Bien que le modèle proposé donne des résultats encore décevants, sûrement en raison du manque de contraintes appropriées pour aider à identifier les paramètres, il a néanmoins ouvert la voie à des perspectives intéressantesArtificial intelligence (AI) has been widely used in the research field of AI-assisted diagnosis, treatment, and personalized medicine. This manuscript focuses on the application of artificial intelligence methods including deep learning and radiomics in cancer diagnosis. First, effective image segmentation is essential for cancer diagnosis and further radiomics-based analysis. We proposed a new approach for automatic lesion segmentation in ultrasound images, based on a multicentric and multipathology dataset displaying different types of cancers. By introducing the group convolution, we proposed a lightweight U-net network without sacrificing the segmentation performance. Second, we processed the clinical Magnetic Resonance Imaging (MRI) images to noninvasively predict the glioma subtype as defined by the tumor grade, isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status. We proposed a radiomics-based approach. The prediction performance improved significantly by tuning different settings in the radiomics pipeline. The characteristics of the radiomic features that best distinguish the glioma subtypes were also analyzed. This work not only provided a radiomics pipeline that works well for predicting the glioma subtype, but it also contributed to the model development and interpretability. Third, we tackled the challenge of reproducibility in radiomics methods. We investigated the impact of different image preprocessing methods and harmonization methods (including intensity normalization and ComBat harmonization) on the radiomic feature reproducibility in MRI radiomics. The conclusion showed that ComBat method is essential to remove the nonbiological variation caused by different image acquisition settings (namely, scanner effects) and improve the feature reproducibility in radiomics studies. Meanwhile, intensity normalization is also recommended because it leads to more comparable MRI images and more robust harmonization results. Finally, we investigated improving the ComBat harmonization method by changing its assumption to a very common case that scanner effects are different for different classes (like tumors and normal tissues). Although the proposed model yielded disappointing results, surely due to the lack of enough proper constraints to help identify the parameters, it still paved the way for the development of new harmonization methods
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Cohen, Emmanuel. "Cartographie, analyse et reconnaissance de réseaux vasculaires par Doppler ultrasensible 4D." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLED046/document.
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Le Doppler ultrasensible est une nouvelle technique d'imagerie ultrasonore permettant d'observer les flux sanguins avec une résolution très fine et sans agent de contraste. Appliquée à l'imagerie microvasculaire cérébrale des rongeurs, cette méthode produit de très fines cartes vasculaires 3D du cerveau à haute résolution spatiale. Ces réseaux vasculaires contiennent des structures tubulaires caractéristiques qui pourraient servir de points de repère pour localiser la position de la sonde ultrasonore et tirer parti des avantages pratiques des appareils à ultrason. Ainsi, nous avons développé un premier système de neuronavigation chez les rongeurs basé sur le recalage automatique d'images cérébrales. En utilisant des méthodes d’extraction de chemins minimaux, nous avons développé une nouvelle méthode isotrope de segmentation pour l’analyse géométrique des réseaux vasculaires en 3D. Cette méthode a été appliquée à la quantification des réseaux vasculaires et a permis le développement d'algorithmes de recalage de nuages de points pour le suivi temporel de tumeursUltrasensitive Doppler is a new ultrasound imaging technique allowing the observation of blood flows with a very fine resolution and no contrast agent. Applied to cerebral microvascular imaging in rodents, this method produces very fine vascular 3D maps of the brain at high spatial resolution. These vascular networks contain characteristic tubular structures that could be used as landmarks to localize the position of the ultrasonic probe and take advantage of the easy-to-use properties of ultrasound devices such as low cost and portability. Thus, we developed a first neuronavigation system in rodents based on automatic registration of brain images. Using minimal path extraction methods, we developed a new isotropic segmentation framework for 3D geometric analysis of vascular networks (extraction of centrelines, diameters, curvatures, bifurcations). This framework was applied to quantify brain and tumor vascular networks, and finally leads to the development of point cloud registration algorithms for temporal monitoring of tumors
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Quartararo, John David. "Semi-automated segmentation of 3D medical ultrasound images." Worcester, Mass. : Worcester Polytechnic Institute, 2008. http://www.wpi.edu/Pubs/ETD/Available/etd-020509-161314/.
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Thesis (M.S.)--Worcester Polytechnic Institute.Keywords: 3d ultrasound; ultrasound; image processing; image segmentation; 3d image segmentation; medical imaging Includes bibliographical references (p.142-148).
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Chalana, Vikram. "Deformable models for segmentation of medical ultrasound images /." Thesis, Connect to this title online; UW restricted, 1996. http://hdl.handle.net/1773/8025.
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Gjedrem, Stian Dalene, and Gunn Marie Navestad. "Segmentation of Neuro Tumours : from MR and Ultrasound images." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2005. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9195.
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We have implemented and tested segmentation methods for segmenting brain tumours from magnetic resonance (MR) and ultrasound data. Our work in this thesis mainly focuses on active contours, both parametric (snakes) and geometric contours (level set). Active contours have the advantage over simpler segmentation methods that they are able to take both high- and low-level information into consideration. This means that the result they produce both depends on shape as well as intensity information from the input image. Our work is based on the results from an earlier completed depth study which investigated different segmentation methods. We have implemented and tested one simplified gradient vector flow snake model and four level set approaches: fast marching level set, geodesic level set, canny edge level set, and Laplacian level set. The methods are evaluated based on precision of the region boundary, sensitivity to noise, the effort needed to adjust parameters and the time to perform the segmentation. We have also compared the results with the result from a region growing method. We achieved promising results for active contour segmentation methods compared with other, simpler segmentation methods. The simplified snake model has given promising results, but has to be subject to more testing. Furthermore, tests with four variants of the level set method have given good results in most cases with MR data and in some cases with ultrasound data.
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Shan, Juan. "A Fully Automatic Segmentation Method for Breast Ultrasound Images." DigitalCommons@USU, 2011. https://digitalcommons.usu.edu/etd/905.
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Breast cancer is the second leading cause of death of women worldwide. Accurate lesion boundary detection is important for breast cancer diagnosis. Since many crucial features for discriminating benign and malignant lesions are based on the contour, shape, and texture of the lesion, an accurate segmentation method is essential for a successful diagnosis. Ultrasound is an effective screening tool and primarily useful for differentiating benign and malignant lesions. However, due to inherent speckle noise and low contrast of breast ultrasound imaging, automatic lesion segmentation is still a challenging task. This research focuses on developing a novel, effective, and fully automatic lesion segmentation method for breast ultrasound images. By incorporating empirical domain knowledge of breast structure, a region of interest is generated. Then, a novel enhancement algorithm (using a novel phase feature) and a newly developed neutrosophic clustering method are developed to detect the precise lesion boundary. Neutrosophy is a recently introduced branch of philosophy that deals with paradoxes, contradictions, antitheses, and antinomies. When neutrosophy is used to segment images with vague boundaries, its unique ability to deal with uncertainty is brought to bear. In this work, we apply neutrosophy to breast ultrasound image segmentation and propose a new clustering method named neutrosophic l-means. We compare the proposed method with traditional fuzzy c-means clustering and three other well-developed segmentation methods for breast ultrasound images, using the same database. Both accuracy and time complexity are analyzed. The proposed method achieves the best accuracy (TP rate is 94.36%, FP rate is 8.08%, and similarity rate is 87.39%) with a fairly rapid processing speed (about 20 seconds). Sensitivity analysis shows the robustness of the proposed method as well. Cases with multiple-lesions and severe shadowing effect (shadow areas having similar intensity values of the lesion and tightly connected with the lesion) are not included in this study.
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Mattsson, Per, and Andreas Eriksson. "Segmentation of Carotid Arteries from 3D and 4D Ultrasound Images." Thesis, Linköping University, Department of Electrical Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1141.
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This thesis presents a 3D semi-automatic segmentation technique for extracting the lumen surface of the Carotid arteries including the bifurcation from 3D and 4D ultrasound examinations.
Ultrasound images are inherently noisy. Therefore, to aid the inspection of the acquired data an adaptive edge preserving filtering technique is used to reduce the general high noise level. The segmentation process starts with edge detection with a recursive and separable 3D Monga-Deriche-Canny operator. To reduce the computation time needed for the segmentation process, a seeded region growing technique is used to make an initial model of the artery. The final segmentation is based on the inflatable balloon model, which deforms the initial model to fit the ultrasound data. The balloon model is implemented with the finite element method.
The segmentation technique produces 3D models that are intended as pre-planning tools for surgeons. The results from a healthy person are satisfactory and the results from a patient with stenosis seem rather promising. A novel 4D model of wall motion of the Carotid vessels has also been obtained. From this model, 3D compliance measures can easily be obtained.
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Chiu, Edwin. "Lossy compression of medical ultrasound images using space-frequency segmentation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0027/MQ51315.pdf.
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Muzzolini, Russell E. "A volumetric approach to segmentation and texture characterisation of ultrasound images." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq24041.pdf.
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Anxionnat, Adrien. "Segmentation of high frequency 3D ultrasound images for skin disease characterization." Thesis, KTH, Teknisk informationsvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209203.
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This work is rooted in a need for dermatologists to explore skin characteristicsin depth. The inuence of skin disease such as acne in dermal tissues is stilla complex task to assess. Among the possibilities, high frequency ultrasoundimaging is a paradigm shift to probe and characterizes upper and deep dermis.For this purpose, a cohort of 58 high-frequency 3D images has been acquiredby the French laboratory Pierre Fabre in order to study acne vulgaris disease.This common skin disorder is a societal challenge and burden aecting late adolescentsacross the world. The medical protocol developed by Pierre Fabre wasto screen a lesion every day during 9 days for dierent patients with ultrasoundimaging. The provided data features skin epidermis and dermis structure witha fantastic resolution. The strategy we led to study these data can be explainedin three steps. First, epidermis surface is detected among artifacts and noisethanks to a robust level-set algorithm. Secondly, acne spots are located on theresulting height map and associated to each other among the data by computingand thresholding a local variance. And eventually potential inammatorydermal cavities related to each lesion are geometrically and statistically characterizedin order to assess the evolution of the disease. The results presentan automatic algorithm which permits dermatologists to screen acne vulgarislesions and to characterize them in a complete data set. It can hence be a powerfultoolbox to assess the eciency of a treatment.Detta arbete är grundat i en dermatologs behov att undersöka hudens egenskaperpå djupet. Påverkan av hudsjukdomar så som acne på dermala vävanderär fortfarande svårt att bedöma. Bland möjligheterna är högfrekvent ultraljudsavbildningett paradigmskifte för undersökning och karakterisering av övre ochdjupa dermis. I detta syfte har en kohort av 58 högfrekventa 3D bilder förvärvatsav det Franska laboratoriet Pierre Fabre för att studera sjukdomen acne vulgaris.Denna vanliga hudsjukdom är en utmaning för samhället och en bördasom påverkar de i slutet av tonåren över hela världen. Protokollet utvecklatav Pierre Fabre innebar att undersöka en lesion varje dag över 9 dagar förolika patienter med ultraljudavbildning. Den insamlade datan visar hudens epidermisoch dermis struktur med en fantastiskt hög upplösning. Strategin vianvände för att studera denna data kan förklaras i tre steg. För det första,hittas epidermis yta bland artifakter och brus tack vare en robust level-set algoritm.För det andra, acne äckar hittas på höjdkartan och associeras tillvarandra bland mätdatan genom en tröskeljämförelse över lokala variationer.Även potentiellt inammatoriska dermala hålrum relaterade till varje lesion blirgeometriskt ochj statistiskt kännetecknade för att bedöma sjukdomens förlopp.Resultaten framför en automatisk algoritm som gör det möjligt för dermatologeratt undersöka acne vulgaris lesioner och utmärka de i ett dataset. Detta kandärmed vara en kraftfull verktygslåda för att undersöka inverkan av en behandlingtill denna sjukdom.
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Pathak, Sayan Dev. "Computer-aided segmentation of anatomical features in transrectal ultrasound prostate images /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/8125.
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Marcomini, Karem Daiane. "Caracterização de lesões em imagens digitais de ultrassonografia e elastografia da mama utilizando técnicas inteligentes." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/18/18152/tde-08122017-113952/.
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Muitos procedimentos vêm sendo desenvolvidos para auxiliar no diagnóstico precoce do câncer de mama. Devido a subjetividade na interpretação de imagens, os sistemas de diagnóstico auxiliado por computador (CADx) têm oferecido ao especialista uma segunda opinião mais precisa e confiável. Nesse propósito, essa pesquisa apresenta uma metodologia de investigação da potencialidade diagnóstica de um sistema computacional na classificação de achados suspeitos em imagens de ultrassom modo-B e de elastografia da mama. A base de dados foi constituída por 31 lesões malignas e 52 benignas e um conjunto adicional contendo 206 lesões de ultrassom modo-B (144 benignas e 62 malignas) para a realização dos testes de aprendizado de máquina. O contorno foi determinado automaticamente e através do delineamento manual de três radiologistas sob a imagem de ultrassom modo-B e, em seguida, mapeado na imagem elastográfica. As lesões foram classificadas pelo sistema CADx desenvolvido para ultrassom modo-B e elastografia do tipo strain. Os dados foram avaliados por meio da sensibilidade, especificidade e AUC. O sistema CADx desenvolvido proporcionou equivalência diagnóstica para a classificação das lesões a partir das diversas formas de determinação do contorno (manual e automática), permitindo a redução da variabilidade. Além disso, o sistema apontou resultados superiores à análise visual do radiologista que, quando considerado o resultado fornecido pela associação entre as imagens de ultrassom modo-B e elastografia, proporcionou um aumento comparativo de cerca de 7% em sensibilidade e 17,2% em especificidade nos testes com o sistema CADx usando o contorno feito pelo radiologista mais experiente. Além disso, constatou-se uma influência positiva no uso da ferramenta computacional pelos radiologistas, pois, na média, seus índices de sensibilidade e especificidade diagnóstica aumentaram também em relação à situação de análise convencional, passando de 87,1% e 55,8% para 90,3% e 73,1%, respectivamente.Many procedures have been developed to aid in the early detection and diagnosis of breast cancer. In this context, Computer-Aided Diagnosis (CADx) systems were designed to provide to the specialist a reliable second opinion. This study presents the proposal of investigating the diagnostic ability of a computational system in the characterization of suspicious findings in B-mode ultrasound and breast elastography imaging. The database consisted of 31 malignant and 52 benign lesions and an additional data set containing 206 lesions (144 benign and 62 malignant) seen only on the B-mode ultrasound for performing the machine learning tests. Three radiologists drew manually the contour of the lesions in B-mode ultrasound and we used an automatic technique to segment the lesions. Then, the contour was mapped in the elastography image. The lesions were classified using the CADx system developed for B-mode ultrasound and strain elastography. We calculated the sensitivity, specificity and AUC to evaluate the data. The developed CADx system provided a diagnostic concordance in the classification of breast lesions from the different ways of contour determination (manual and automatic), allowing to reduce the diagnostic variability. In addition, the CADx system showed superior results to the visual analysis of the radiologist. When the radiologist associated both examinations (B-mode ultrasound and elastography), his visual analysis provided 87.10%, 55.77% and 0.714 of sensitivity, specificity and AUC, respectively. When we considered the result provided by the association between B-mode ultrasound and elastography images, the CADx system provided a comparative increase of about 7% of sensitivity and 17.2% of specificity, using the contour delimited by the most experienced radiologist. In addition, a positive influence was observed in the use of the computational tool by radiologists, since, on average, their sensitivity and specificity indexes also increased in relation to the conventional analysis, from 87.1% and 55.8% to 90.3% and 73.1%, respectively.
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Zahnd, Guillaume. "Estimation du mouvement bi-dimensionnel de la paroi artérielle en imagerie ultrasonore par une approche conjointe de segmentation et de speckle tracking." Phd thesis, INSA de Lyon, 2012. http://tel.archives-ouvertes.fr/tel-00835828.
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Ce travail de thèse est axé sur le domaine du traitement d'images biomédicales. L'objectif de notre étude est l'estimation des paramètres traduisant les propriétés mécaniques de l'artère carotide in vivo en imagerie échographique, dans une optique de détection précoce de la pathologie cardiovasculaire. L'analyse du mouvement longitudinal des tissus de la paroi artérielle, i.e. dans la même direction que le flux sanguin, représente la motivation majeure de ce travail. Les trois contributions principales proposées dans ce travail sont i) le développement d'un cadre méthodologique original et semi-automatique, dédié à la segmentation et à l'estimation du mouvement de la paroi artérielle dans des séquences in vivo d'images ultrasonores mode-B, ii) la description d'un protocole de génération d'une référence, faisant intervenir les opérations manuelles de plusieurs experts, dans le but de quantifier la précision des résultats de notre méthode malgré l'absence de vérité terrain inhérente à la modalité échographique, et iii) l'évaluation clinique de l'association entre les paramètres mécaniques et dynamiques de la paroi carotidienne et les facteurs de risque cardiovasculaire dans le cadre de la détection précoce de l'athérosclérose. Nous proposons une méthode semi-automatique, basée sur une approche conjointe de segmentation des contours de la paroi et d'estimation du mouvement des tissus. L'extraction de la position des interfaces est réalisée via une approche spécifique à la structure morphologique de la carotide, basée sur une stratégie de programmation dynamique exploitant un filtrage adapté. L'estimation du mouvement est réalisée via une méthode robuste de mise en correspondance de blocs (block matching), basée sur la connaissance du déplacement a priori ainsi que sur la mise à jour temporelle du bloc de référence par un filtre de Kalman spécifique. La précision de notre méthode, évaluée in vivo, correspond au même ordre de grandeur que celle résultant des opérations manuelles réalisées par des experts, et reste sensiblement meilleure que celle obtenue avec deux autres méthodes traditionnelles (i.e. une implémentation classique de la technique de block matching et le logiciel commercial Velocity Vector Imaging). Nous présentons également quatre études cliniques réalisées en milieu hospitalier, où nous évaluons l'association entre le mouvement longitudinal et les facteurs de risque cardiovasculaire. Nous suggérons que le mouvement longitudinal, qui représente un marqueur de risque émergent et encore peu étudié, constitue un indice pertinent et complémentaire aux marqueurs traditionnels dans la caractérisation de la physiopathologie artérielle, reflète le niveau de risque cardiovasculaire global, et pourrait être bien adapté à la détection précoce de l'athérosclérose.
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Garnier, Carole. "Segmentation de la prostate pour la thérapie par Ultrasons Haute Intensité guidée par l'image." Phd thesis, Université Rennes 1, 2009. http://tel.archives-ouvertes.fr/tel-00498035.
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Ce travail traite le problème de la segmentation d'images échographiques de prostate acquises en condition per-opératoire dans le cadre de la destruction de tumeurs effectuée par une technique d'ultrasons haute intensité (HIFU). L'objectif est de délimiter précisément les tissus cible de façon à concentrer l'échauffement induit par les ultrasons tout en réduisant leur impact sur les structures voisines. L' étude bibliographique de l'état de l'art montre que toutes les méthodes de segmentation se référant aux dernières avancées méthodologiques ont été tentées sans pour autant apporter de réponses complètement satisfaisantes au problème du fait de la variabilité des situations rencontrées et surtout de la qualité toute relative des images dans le cas des HIFU. Les différentes solutions proposées dans cette thèse s'appuient sur les modèles déformables discrets enrichis de recherche de points d'ancrage basés gradient, couplés ou pas à une approche de détection de surface optimale. Ces solutions sont testées sur une trentaine de bases de données et analysées à la fois qualitativement et quantitativement par comparaison à des contours définis par des experts. Par ailleurs, une étude préliminaire est conduite sur la caractérisation de texture par différents types de moments (Zernike, Legendre, etc.). Les résultats obtenus montrent un comportement globalement correct et satisfaisant les temps de calcul imposés.
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Massich, i. Vall Joan. "Deformable object segmentation in ultra-sound images." Doctoral thesis, Universitat de Girona, 2013. http://hdl.handle.net/10803/128329.
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This thesis analyses the current strategies to segment breast lesions in Ultra-Sound (US) data and proposes a fully automatic methodology for generating accurate segmentations of breast lesions in US data with low false positive rates. The proposed approach targets the segmentation as a minimization procedure for a multi-label probabilistic framework that takes advantage of min-cut/max- flow Graph-Cut (GC) minimization for inferring the appropriate label from a set of tissue labels for all the pixels within the target image. The image is divided into contiguous regions so that all the pixels belonging to a particular region would share the same label by the end of the process. From a training image dataset stochastic models are built in order to infer a label for each region of the image. The main advantage of the proposed framework is that it splits the problem of segmenting the tissues present in US the images into subtasks that can be taken care of individuallyEn aquest treball, es proposa un sistema automàtic per generar delineacions acurades de lesions de mama en imatges d’ultrasò. El sistema proposat planteja el problema de trobar la delineació corresponent a la minimització d’un sistema probabilístic multiclasse mitjançant el tall de mínim cost del graf que representa la imatge. El sistema representa la imatge com un conjunt de regions i infereix una classe per cada una d’aquestes regions a partir d’uns models estadístics obtinguts d’unes imatges d’entrenament. El principal avantatge del sistema és que divideix la tasca en subtasques més fàcils d’adreçar i després soluciona el problema de forma global
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Zhao, Fangwei. "Multiresolution analysis of ultrasound images of the prostate." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2004. http://theses.library.uwa.edu.au/adt-WU2004.0028.
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[Truncated abstract] Transrectal ultrasound (TRUS) has become the urologist’s primary tool for diagnosing and staging prostate cancer due to its real-time and non-invasive nature, low cost, and minimal discomfort. However, the interpretation of a prostate ultrasound image depends critically on the experience and expertise of a urologist and is still difficult and subjective. To overcome the subjective interpretation and facilitate objective diagnosis, computer aided analysis of ultrasound images of the prostate would be very helpful. Computer aided analysis of images may improve diagnostic accuracy by providing a more reproducible interpretation of the images. This thesis is an attempt to address several key elements of computer aided analysis of ultrasound images of the prostate. Specifically, it addresses the following tasks: 1. modelling B-mode ultrasound image formation and statistical properties; 2. reducing ultrasound speckle; and 3. extracting prostate contour. Speckle refers to the granular appearance that compromises the image quality and resolution in optics, synthetic aperture radar (SAR), and ultrasound. Due to the existence of speckle the appearance of a B-mode ultrasound image does not necessarily relate to the internal structure of the object being scanned. A computer simulation of B-mode ultrasound imaging is presented, which not only provides an insight into the nature of speckle, but also a viable test-bed for any ultrasound speckle reduction methods. Motivated by analysis of the statistical properties of the simulated images, the generalised Fisher-Tippett distribution is empirically proposed to analyse statistical properties of ultrasound images of the prostate. A speckle reduction scheme is then presented, which is based on Mallat and Zhong’s dyadic wavelet transform (MZDWT) and modelling statistical properties of the wavelet coefficients and exploiting their inter-scale correlation. Specifically, the squared modulus of the component wavelet coefficients are modelled as a two-state Gamma mixture. Interscale correlation is exploited by taking the harmonic mean of the posterior probability functions, which are derived from the Gamma mixture. This noise reduction scheme is applied to both simulated and real ultrasound images, and its performance is quite satisfactory in that the important features of the original noise corrupted image are preserved while most of the speckle noise is removed successfully. It is also evaluated both qualitatively and quantitatively by comparing it with median, Wiener, and Lee filters, and the results revealed that it surpasses all these filters. A novel contour extraction scheme (CES), which fuses MZDWT and snakes, is proposed on the basis of multiresolution analysis (MRA). Extraction of the prostate contour is placed in a multi-scale framework provided by MZDWT. Specifically, the external potential functions of the snake are designated as the modulus of the wavelet coefficients at different scales, and thus are “switchable”. Such a multi-scale snake, which deforms and migrates from coarse to fine scales, eventually extracts the contour of the prostate
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Provent, Pierre. "Segmentation d'images par analyse statistique de textures : application aux images échocardiographiques." Paris 12, 1991. http://www.theses.fr/1991PA120049.
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Une etape de segmentation de l'image echographique s'avere necessaire pour en extraire les informations quantitatives utiles pour l'etude de la fonction cardiaque comme: la fraction d'ejection ventriculaire, les variations de volume myocardique pendant un cycle,. . . Nous proposons de caracteriser chaque region de l'image par ses attributs optimaux de texture que l'on determine par une etude statistique (analyse en composantes principales) des proprietes de structure locale des pixels dans un voisinage donne. Le choix d'une formulation statistique de l'analyse de texture permet un traitement unifie des micro et macrotextures. La pertinence des attributs est verifiee par une analyse factorielle discriminante sur des textures de synthese naturelles et echographiques. Une procedure de classement automatique par apprentissage effectue la segmentation par regroupement des pixels de caracteristiques homogenes. L'hypothese d'une distribution gaussienne multivariee conduit a la definition d'une regle d'affectation optimale des individus (critere de bayes) fondee sur la distance de mahalanobis. Des solutions algorithmiques rapides sont proposees pour le calcul des attributs et de la distance de mahalanobis. Ce travail presente aussi une etude des differents problemes lies a l'acquisition et la formation des images echographiques sectorielles, et propose des solutions pour adapter un certain nombre d'operateurs classiques de traitement d'images, en coordonnees polaires: operateurs de derivation du gradient directionnel et laplacien, operateurs de sobel et de prewitt ainsi que des operateurs d'amelioration d'images comme le filtre median et moyenneur
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Selagamsetty, Srinivasa Siddhartha. "Exploring a Methodology for Segmenting Biomedical Images using Deep Learning." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1573812579683504.
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Sandoval, Niño Zulma. "Planning and guidance of ultrasound guided High Intensity Focused Ultrasound cardiac arrhythmia therapy." Thesis, Rennes 1, 2015. http://www.theses.fr/2015REN1S044/document.
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L'objectif des travaux présentés dans ce document est de développer de nouvelles méthodes de traitement d'images pour améliorer la planification et le guidage d'une thérapie par voie transœsophagienne de la fibrillation auriculaire à l'aide d'Ultrason Focalisé Haute Intensité. Le document est divisé en deux parties : la planification du traitement et le guidage de la thérapie. Pour la planification de la thérapie, l'idée est d'exploiter l'information acquise au stade préopératoire par un scanner X ou IRM afin de retrouver l'anatomie spécifique du patient et à y définir le futur geste thérapeutique. Plus particulièrement, nos différentes contributions ont porté sur une approche multi-atlas de segmentation de l'oreillette gauche et des veines pulmonaires ; le tracé des lignes de lésions sur le volume initial ou segmenté ; et la reconstruction d'un volume adapté à la future navigation transœsophagienne. Pour le guidage de la thérapie, nous proposons une nouvelle approche de recalage qui permet d'aligner les images échographiques peropératoires 2D et l'information 3D CT préopératoire. Dans cette approche, dans un premier temps nous avons sélectionné la mesure de similarité la plus adaptée à notre problématique à l'aide d'une évaluation systématique puis nous avons tiré profit des contraintes imposées à la sonde transœsophagienne par l'anatomie du patient pour simplifier la procédure de recalage. Toutes ces méthodes ont été évaluées sur des fantômes numériques ou physiques et sur des données cliniquesThe work presented in this document aims at developing new image-processing methods to improve the planning and guidance of transesophageal HIFU atrial fibrillation therapy. This document is divided into two parts, namely therapy planning and therapy guidance. We first propose novel therapy planning methods that exploit high-resolution pre-operative CT or MRI information to extract patient-specific anatomical details and to define future therapeutic procedures. Our specific methodological contributions concern the following: an automatically-refined atlas-based segmentation approach to extract the left atrium and pulmonary veins; the delineation of the lesion lines on the original or segmented volume; and the reconstruction of a volume adapted to future intraoperative transesophageal navigation. Secondly, our proposal of a novel registration approach for use in therapy guidance aligns intraoperative 2D ultrasound with preoperative 3D CT information. This approach first carries out a systematic statistical evaluation to select the best similarity measure for our application and then takes advantage of the geometrical constraints of the transesophageal HIFU probe to simplify the registration process. Our proposed methods have been evaluated on digital and/or physical phantoms and on real clinical data
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Mohamed, Samar. "Integrated Feature Analysis for Prostate Tissue Characterization Using TRUS Images." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2888.
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The Prostate is a male gland that is located around the urethra. Prostate Cancer is the second most diagnosed malignancy in men over the age of fifty. Typically, prostate cancer is diagnosed from clinical data, medical images, and biopsy. Computer Aided Diagnosis (CAD) was introduced to help in the diagnosis in order to assist in the biopsy operations. Usually, CAD is carried out utilizing either the clinical data, using data mining techniques, or using features extracted from either TransRectal UltraSound (TRUS) images or the Radio Frequency (RF) signals.
The challenge is that TRUS images' quality is usually poor compared to either Magnetic Resonance Imaging (MRI) or the Computed Tomography (CT). On the other hand, ultrasound imaging is more convenient because of its simple instrumentation and mobility capability compared to either CT or MRI. Moreover, TRUS is far less expensive and does not need certain settings compared to either MRI or CT. Accordingly; the main motivation of this research is to enhance the outcome of TRUS images by extracting as much information as possible from it. The main objective of this research is to implement a powerful noninvasive CAD tool that integrates all the possible information gathered from the TRUS images in order to mimic the expert radiologist opinion and even go beyond his visual system capabilities, a process that will in turn assist the biopsy operation. In this sense, looking deep in the TRUS images by getting some mathematical measures that characterize the image and are not visible by the radiologist is required to achieve the task of cancer recognition.
This thesis presents several comprehensive algorithms for integrated feature analysis systems for the purpose of prostate tissue classification. The proposed algorithm is composed of several stages, which are: First, the regions that are highly suspicious are selected using the proposed Gabor filter based ROI identification algorithm.
Second, the selected regions are further examined by constructing different novel as well as typical feature sets. The novel constructed feature sets are composed of statistical feature sets, spectral feature sets and model based feature sets.
Next, the constructed features were further analyzed by selecting the best feature subset that identifies the cancereus regions. This task is achieved by proposing different dimensionality reduction methods which can be categorized into: Classifier dependent feature selection (Mutual Information based feature selection), classifier independent feature selection, which is based mainly on tailoring the Artificial life optimization techniques to fit the feature selection problem and Feature Extraction, which transforms the data to a new lower dimension space without any degradation in the information and with no correlation among the transformed lower dimensional features.
Finally, the last proposed fragment in this thesis is the Spectral Clustering algorithm, which is applied to the TRUS images. Spectral Clustering is a novel fast algorithm that can be used in order to obtain a fast initial estimate of the cancer regions. Moreover, it can be used to support the decision obtained by the proposed cancer recognition algorithm. This decision support process is crucial at this stage as the gold standards used in obtaining the results shown in this thesis is mainly the radiologist's markings on the TRUS images. This gold standards is not considered as credible since the radiologist's best accuracy is approximately 65 %.
In conclusion, this thesis introduces different novel complete algorithms for automatic cancerous regions detection in the prostate gland utilizing TRUS images. These proposed algorithms complement each other in which the results obtained using either of the proposed algorithms support each other by resulting in the same classification accuracy, sensitivity and specificity. This result proves the remarkable quality of the constructed features as well as the superiority of the introduced feature selection and feature extraction methods to detect cancerous regions in the prostate gland.
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Nguyen, Khac Lan. "Modèles de champ de phase et modèles Lattice Boltzmann pour la segmentation 3D de tumeurs en imagerie ultrasons hautes fréquences." Thesis, La Rochelle, 2019. http://www.theses.fr/2019LAROS011.
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Nous nous intéressons dans cette thèse au problème de la segmentation 3D de tumeurs de la peau dans des images ultrasons hautes fréquences. Nous nous concentrons essentiellement sur deux questions : comment estimer au mieux le volume des tumeurs (en accord avec les références produites par des dermatologues) et comment produire des algorithmes dont les temps de calcul se rapprochent du temps réel ? Dans un premier temps, nous décrivons un nouvel modèle, log-likelihood Cahn-Hilliard (LLCH), basé sur une formulation variationnelle couplant un terme d’attache aux données calculé à partir d’estimations non paramétriques et un terme de régularisation issu d’une dynamique de transitions de phase (équation de réaction diffusion d’Allen Cahn). Ce modèle est testé avec une première implémentation multigrille par solutions exactes calculées grâce à un splitting de Lie. Dans un second temps, nous nous intéressons à la possibilité d’implémenter le modèle LLCH par des méthodes Lattice Boltzmann (LBM). La dynamique sous-jacente n’étant pas de nature physique, cette implémentation n’est pas directe et est sujette à des problèmes d’instabilité. Nous montrons que, compte tenu des spécificités du terme d’attache aux données, les schémas BGK, à simple temps de relaxation, ne permettent pas d’assurer une stabilité suffisante. Nous avons alors recours à des schémas MRT, à temps de relaxation multiples, qui permettent par l’introduction de paramètres additionnels de gagner en stabilité. L’ajustement des paramètres dits quartiques permet d’obtenir des schémas exacts à l’ordre 4 et numériquement stables. Les tests réalisés sur une base de données cliniques avec une vérité terrain fournie par des dermatologues montrent que les résultats obtenus grâce aux deux implémentations proposées sont bien meilleurs que ceux obtenus par les méthodes level sets et que notre modèle est une bonne alternative pour pallier le problème de la sous-estimation du volume tumoral. Les temps de calcul, pour des images 3D d’environ 70 millions de voxels, sont très courts et tout-à-fait adaptés pour une utilisation pratique en milieu médicalIn this thesis, we are interested in the problem of 3D segmentation of skin tumors in high frequency ultrasound images. We focus mainly on two questions: how best to estimate the volume of tumors (in accordance with references produced by dermatologists) and how to produce algorithms whose computation times are close to real time? First, we describe a new model, log-likelihood Cahn-Hilliard (LLCH), based on a variational formulation coupling a data attachment term computed from non-parametric estimates and a regularization term derived from a phase transition dynamic (Allen Cahn reaction diffusion equation). This model is tested with a first multigrid implementation using exact solutions calculated with a Lie splitting. Secondly, we are interested in the possibility of implementing the LLCH model using lattice Boltzmann methods (LBM). The underlying dynamic is not physical in nature, so this implementation is not direct and is subject to instability problems. We show that, due to the specificities of the data attachment term, the BGK schemes, with simple relaxation time, do not ensure sufficient stability. We then use MRT schemes, with multiple relaxation times, which allow us to gain stability by introducing additional parameters. The adjustment of the so-called quartic parameters makes it possible to obtain fourth-order exact schemes that are numerically stable. Tests performed on a clinical database with ground truth provided by dermatologists show that the results obtained with the two proposed implementations are much better than those obtained with level sets methods and that our model is a good alternative to overcome the problem of underestimation of tumor volume. The computation times, for 3D images of about 70 million voxels, are very short and well adapted for practical use in medical environments
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Dahdouh, Sonia. "Filtrage, segmentation et suivi d'images échographiques : applications cliniques." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00647326.
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La réalisation des néphrolithotomies percutanées est essentiellement conditionnée par la qualité dela ponction calicièle préalable. En effet, en cas d'échec de celle-ci, l'intervention ne peut avoir lieu.Réalisée le plus souvent sous échographie, sa qualité est fortement conditionnée par celle du retouréchographique, considéré comme essentiel par la deuxième consultation internationale sur la lithiase pour limiter les saignements consécutifs à l'intervention.L'imagerie échographique est largement plébiscitée en raison de son faible coût, de l'innocuité del'examen, liée à son caractère non invasif, de sa portabilité ainsi que de son excellente résolutiontemporelle ; elle possède toutefois une très faible résolution spatiale et souffre de nombreux artefacts tels que la mauvaise résolution des images, un fort bruit apparent et une forte dépendance àl'opérateur.L'objectif de cette thèse est de concevoir une méthode de filtrage des données échographiques ainsiqu'une méthode de segmentation et de suivi du rein sur des séquences ultrasonores, dans le butd'améliorer les conditions d'exécution d'interventions chirurgicales telles que les néphrolithotomiespercutanées.Le filtrage des données, soumis et publié dans SPIE 2010, est réalisé en exploitant le mode deformation des images : le signal radiofréquence est filtré directement, avant même la formation del'image 2D finale. Pour ce faire, nous utilisons une méthode basée sur les ondelettes, en seuillantdirectement les coefficients d'ondelettes aux différentes échelles à partir d'un algorithme de typesplit and merge appliqué avant reconstruction de l'image 2D.La méthode de suivi développée (une étude préliminaire a été publiée dans SPIE 2009), exploiteun premier contour fourni par le praticien pour déterminer, en utilisant des informations purementlocales, la position du contour sur l'image suivante de la séquence. L'image est transformée pourne plus être qu'un ensemble de vignettes caractérisées par leurs critères de texture et une premièresegmentation basée région est effectuée sur cette image des vignettes. Cette première étape effectuée, le contour de l'image précédente de la séquence est utilisé comme initialisation afin de recalculer le contour de l'image courante sur l'image des vignettes segmentée. L'utilisation d'informations locales nous a permis de développer une méthode facilement parallélisable, ce qui permettra de travailler dans une optique temps réel.La validation de la méthode de filtrage a été réalisée sur des signaux radiofréquence simulés. Laméthode a été comparée à différents algorithmes de l'état de l'art en terme de ratio signal sur bruitet de calcul de USDSAI. Les résultats ont montré la qualité de la méthode proposée comparativement aux autres. La méthode de segmentation, quant-à elle, a été validée sans filtrage préalable, sur des séquences 2D réelles pour un temps d'exécution sans optimisation, inférieur à la minute pour des images 512*512.
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Hesko, Branislav. "Aktivní kontury pro segmentaci ultrazvukových dat." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221388.
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This diploma thesis aims to implement an active contour method for ultrasound image segmentation. Properties of ultrasound images, basic segmentation approaches and a~principle of choosen active contour methods are described within theoretical part. Two different groups of active contour methods exists, methods with use of gradient and without use of gradient as image feature. For comparision, one method of each group is implemented in practical part and subsequently, segmentation efficiency and properties of methods are compared in evaluation part.
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Fouquet, Clément. "Aide à la détection et à la reconnaissance de défauts structurels dans les pipelines par analyse automatique des images XtraSonic." Thesis, Cergy-Pontoise, 2014. http://www.theses.fr/2014CERG0729/document.
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TRAPIL est une société Française ayant à charge l'exploitation et l'entretien de pipelines d'hydrocarbures. L'entretien de pipelines enterrés nécessite le passage de racleurs équipés de sondes ultrasons réalisant une cartographie de la structure du pipeline, qui est ensuite analysée à la main afin de détecter et d'identifier les différents défauts pouvant apparaître ou évoluer.L'objectif de ce travail de thèse est d'apporter une solution algorithmique permettant d'accélérer et de compléter le travail des analystes à l'aide des méthodes modernes de traitement d'images et du signal.Notre approche suit le mode opératoire des experts et est découpée en trois partie.Tout d'abord nous réalisons une détection des soudures d'aboutage permettant de séparer le pipelines en les différents tubes qui le composent. Les signaux de sondes représentant la circonférence du tube sont regroupés et compressés dans une détection de rupture par comparaison de moyenne à court et long terme, puis les signaux résultants sont fusionnés à l'aide d'une pondération unique permettant une augmentation majeure du contraste entre bruit et soudure, offrant une détection et une localisation presque sans faille.Les tubes subissent ensuite une première segmentation visant à éliminer le plus grand nombre de pixels sains. Usant de modélisation d'histogramme des valeurs d'épaisseur par un algorithme EM initialisé pour notre problématique, l'algorithme suit un principe récursif comparable aux méthodes de type split and merge pour détecter et isoler les zones dangereuses.Enfin, Les zones dangereuses sont identifiées à l'aide d'une foret aléatoire, apprise sur un grand nombre d'exemples de défauts. Cette troisième partie est centrée sur l'étude de différentes méthodes de reconnaissance de forme appliquées à notre nouvelle problématique.Au travers de ces différentes étapes, les solutions que nous avons apportées permettent à TRAPIL un gain de temps significatif sur les tâches les plus fastidieuses du processus d'analyse (par exemple 30% sur la détection de soudures) et leur offre de nouvelles possibilités commerciales, par exemple la possibilité de fournir un pré-rapport à leur clientèle en quelques jours pendant que l'analyse manuelle est réalisée pouvant prendre plus d'un moisTRAPIL is a French society who is in charge of exploitation and maintenance of oil pipelines. Maintenance of buried pipeline implies the use of ultrasonic sensor-equipped devices, providing thickness and structural maps of the pipe, which are analysed by experts in order to detect and identify defects that may appear or evolve.The objective of this work is to provide an algoritmic solution allowing to accelerate and aid the experts's work with modern image and signal processing methods.Our approach follows the experts's operating mode and is divided in three sections.First, a weld detection is realized allowing to split the pipe in tubes. The signals of probes representing the circumference of the pipe are regrouped and compressed through an abrupt change detection, using short and long-term average comparison, then the resulting signals are merged using a unique weightening function allowing a massive increase of the contrast between welds and noise, offering near-perfect detection and localization.The tubes then undergoes a first segmentation aiming at eliminating a large amount of sane pixels. Using histogram modelization through an EM algorithm tuned specially for our purpose, the algorithm follows a recursive approach comparable to split and merge methods to detect and isolate dangerous areas.Finally, those dangerous areas are identified with a Random Forest, which has been learnt on a large amount of defect examples. This third part is greatly focused on the study of different pattern recognition methods applied on our new problematic.Through those different steps, the solution we brought allows TRAPIL to save a lot of time on the most tedious tasks of the analysis process (for example 30% of gain in processing time for the weld detection) and offers new commercial possibilities, like for example the possibility to provide their clients a first report in a matter of days, while the manual analysis is completed, which can take more than a month
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Mora, Cofré Marco. "Ensemble de niveaux robustes au speckle et recalage B-spline : application à la segmentation et l'analyse du mouvement cardiaque par des images ultrasons." Toulouse, INPT, 2008. http://ethesis.inp-toulouse.fr/archive/00000658/.
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L'analyse du mouvement local des parois du coeur dans des images ultrasonores est souvent utilisée pour diagnostiquer certaines malformations cardiaques. Malheureusement, cette modalité produit des images caractérisées par un niveau élevé de speckle, rendant difficile la détection des cavités. La thèse présente une méthode d'estimation du mouvement des cavités dans des images 2D. Nous proposons un nouveau modèle de level sets pour segmenter l'image. Ce modèle s'appuie sur une fonction d'arrêt adaptée au speckle. Celle-ci se démarque des fonctions habituelles en remplaçant le gradient par le coefficient de variation, une statistique robuste aux bruits multiplicatifs. De plus, nous renforçant cette fonction par un classificateur perceptron multicouche rendant plus fiable la détection de contours. Les résultats obtenus montrent un apport significatif en précision. L'estimation du mouvement se fait par un processus de recalage adaptatif qui calcule une B-spline hiérarchique. Cette méthode prend en entrée les courbes produites par la segmentation et estime la déformation en appliquant successivement l'algorithme ICP, une optimisation aux moindres carrés, et un raffinage hiérarchique. L'expérimentation montre que ce modèle aboutit à une approximation précise des déformations 2D des parois du coeurAnalysing ultrasound images to estimate local motion of heart walls is widely used to diagnose cardiac malformations. Unfortunately, this modality produces images with a high level of speckle, causing erroneous detection of cavities. This thesis presents a method for estimating heart cavity motion in 2D images. We propose a new level set model to segment cardiac ultrasound images. This model is supported by a stopping term adapted to speckle. Instead of the classical gradient, our stopping term is based on the coeficient of variation. Morever, we improved the detection of contours of this function by adding a supervised clasification based on a Multilayer Perceptron. The obtained results show a significant increase of the precision. The motion estimation is done by means of an adaptative registration process consists of three phases. First, we estimate a linear transform using the ICP algorithm to remove the linear difference between the cavities. The second phase consists of calculating a global B-spline transformation. Finally, we perform a Hierarchical B-spline refinement in regions with unsatisfactory deviations. The experimentations show that our model allows a precise deformation of the heart walls
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Mora, Cofré Marco Ayache Alain. "Ensemble de niveaux robustes au speckle et recalage B-spline application à la segmentation et l'analyse du mouvement cardiaque par des images ultrasons /." Toulouse : INP Toulouse, 2008. http://ethesis.inp-toulouse.fr/archive/00000658.
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Esneault, Simon. "Planning pour la thérapie de tumeur du foie par ultrasons haute intensité." Phd thesis, Université Rennes 1, 2009. http://tel.archives-ouvertes.fr/tel-00497749.
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Dans le contexte général des thérapies minimalement invasives, les travaux de cette thèse portent sur le planning d'une thérapie interstitielle de tumeurs du foie par ultrasons haute intensité. Dans un premier temps, une caractérisation des structures anatomiques hépatiques à partir de données scanner X est proposée selon deux méthodes de segmentation basée sur le graph cut : l'une semi-interactive et rapide pour extraire le foie et les éventuelles tumeurs ; et l'autre automatique et spécifique à la segmentation de la vascularisation hépatique par l'introduction d'un a priori local de forme estimé à partir de moments géométriques 3D. La seconde partie de cette étude est consacrée à la modélisation des effets de la thérapie sur les tissus. Le modèle proposé offre la possibilité de simuler différents types de sonde composée d'une matrice d'éléments contrôlables en phase et intensité. La description de la vascularisation locale dans le milieu peut également être intégrée dans le modèle. Les travaux et résultats obtenus portent sur trois aspects et/ou applications de ce modèle : 1) une méthode pour accélérer la résolution de la BHTE sous certaines hypothèses, 2) des résultats préliminaires de modélisation d'une sonde 64 éléments à focalisation dynamique et 3) le design géométrique d'une sonde endocavitaire 256 éléments.
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Yang, Qing. "Segmentation d'images ultrasonores basée sur des statistiques locales avec une sélection adaptative d'échelles." Phd thesis, Université de Technologie de Compiègne, 2013. http://tel.archives-ouvertes.fr/tel-00869975.
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La segmentation d'images est un domaine important dans le traitement d'images et un grand nombre d'approches différentes ent été développées pendant ces dernières décennies. L'approche des contours actifs est un des plus populaires. Dans ce cadre, cette thèse vise à développer des algorithmes robustes, qui peuvent segmenter des images avec des inhomogénéités d'intensité. Nous nous concentrons sur l'étude des énergies externes basées région dans le cadre des ensembles de niveaux. Précisément, nous abordons la difficulté de choisir l'échelle de la fenêtre spatiale qui définit la localité. Notre contribution principale est d'avoir proposé une échelle adaptative pour les méthodes de segmentation basées sur les statistiques locales. Nous utilisons l'approche d'Intersection des Intervalles de Confiance pour définir une échelle position-dépendante pour l'estimation des statistiques image. L'échelle est optimale dans le sens où elle donne le meilleur compromis entre le biais et la variance de l'approximation polynomiale locale de l'image observée conditionnellement à la segmentation actuelle. De plus, pour le model de segmentation basé sur une interprétation Bahésienne avec deux noyaux locaux, nous suggérons de considérer leurs valeurs séparément. Notre proposition donne une segmentation plus lisse avec moins de délocalisations que la méthode originale. Des expériences comparatives de notre proposition à d'autres méthodes de segmentation basées sur des statistiques locales sont effectuées. Les résultats quantitatifs réalisés sur des images ultrasonores de simulation, montrent que la méthode proposée est plus robuste au phénomène d'atténuation. Des expériences sur des images réelles montrent également l'utilité de notre approche.
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Cheng, Jie-Zhi, and 鄭介誌. "Cell-Based Image Segmentation for 2D and 2D Series Ultrasound Images." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/74833813995255310003.
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碩士國立臺灣大學
醫學工程學研究所
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Boundary information of the object of interest in sonography is the fundamental basis for many clinical studies. It can help to manifest the abnormality of anatomy by characterizing the morphological features and plays the essential role in numerous quantitative ultrasound image analyses. For instance, the evaluation of functional properties of heart demands the quantification of the deformation of epi- and endo-cardiac surfaces. To draw a convincing conclusion for the quantitative analysis, the boundary information should be reliable and efficiently generated― which means robust image segmentation techniques are necessary. This study addresses the challenging segmentation problem of ultrasound images into two parts: 2D and 2D series. Theses two parts are attacked by the two proposed algorithms, i.e. ACCOMP and C2RC-MAP algorithms, respectively. The unique feature of the proposed algorithms is the concept of cell-based. The cell is the catchment basin tessellated by two-pass watershed transformation and is served as the basic operational unit in the two proposed algorithms. Taking the cell tessellation as the basis can be beneficial in three main points. First, comparing to directly finding solutions on pixels, searching on cells is more efficient. It is because the search space spanned by cells is dramatically smaller than the space of pixels. Therefore redundant computation could be saved. Second, the concrete region and edge information can be obtained in the cell tessellation. The concrete information in regions and edges could be valuable clues to assist the segmentation task. Third, as the cell is the group of pixels with homogeneous intensity, it might be more robust to noise in statistics— which could potentially improve the task of image process in ultrasound images. With these three advantages, cell-based image segmentation approaches might be more efficacious and efficient than pixel-based approaches. The ACCOMP algorithm is a two-phase data-driven approach, which is constituted by the partition and the edge grouping phases. The partition phase is purposed to tessellate the image or ROI with prominent components and is carries out by the cell competition algorithm. For the second phase, it is realized by the cell-based graph-traversing algorithm. Focusing on the edge information, the complicated echogenicity problem can be bypassed. The ACCOMP algorithm is validated on 300 breast sonograms, including 165 carcinomas and 135 benign cysts. The results show that more than 70% of the derived boundaries fall within the span of the manually outlines under 95% confident interval. The robustness of reproducibility is confirmed by the Friedman test, the p-values of which is 0.54. It has also suggested that the lesions sizes derived by the ACCOMP algorithm are highly correlated with the lesions defined by the average manually delineated boundaries. To ensure the delineated boundaries of a series of 2D images closely following the visually perceivable edges with high boundary coherence between consecutive slices, the C2RC-MAP algorithm is proposed. It deforms the region boundary in a cell-by-cell fashion through a cell-based two-region competition process. The cell-based deformation is guided by a cell-based MAP framework with a posterior function characterizing the distribution of the cell means in each region, the salience and shape complexity of the region boundary and the boundary coherence of the consecutive slices. The C2RC-MAP algorithm has been validated using 10 series of breast sonograms, including 7 compression series and 3 freehand series. The compression series contains 2 carcinoma and 5 fibroadenoma cases and the freehand series 2 carcinoma and 1 fibroadenoma cases. The results show that more than 70% of the derived boundaries fall within the span of the manually delineated boundaries. The robustness of the proposed algorithm to the variation of ROI is confirmed by the Friedman tests, the p-values of which are 0.517 and 0.352 for the compression and freehand series groups, respectively. The Pearson’s correlations between the lesion sizes derived by the proposed algorithm and those defined by the average manually delineated boundaries are all higher than 0.990. The overlapping and difference ratios between the derived boundaries and the average manually delineated boundaries are mostly higher than 0.90 and lower than 0.13, respectively. For both series groups, all assessments conclude that the boundaries derived by the proposed algorithm be comparable to those delineated manually. Moreover, it is shown that the proposed algorithm is superior to the Chan and Vese level set method based on the paired-sample t-tests on the performance indices at 5% significance level.