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Relevant bibliographies by topics / Ultrasound image segmentation

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Academic literature on the topic 'Ultrasound image segmentation'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Ultrasound image segmentation"

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J. Hemalatha, R., Dr V. Vijaybaskar, A. Josephin Arockia Dhivya, and . "Early detection of joint abnormalities from ultrasound images." International Journal of Engineering & Technology 7, no. 2.25 (May 3, 2018): 105. http://dx.doi.org/10.14419/ijet.v7i2.25.16569.

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Musculoskeletal ultrasound is effective for the early detection of joint abnormalities like erosion, effusion, synovitis and inflammation. Computer software is developed for segmentation of joint ultrasound image to diagnose the defect. The objective of developing this paper is to achieve early diagnosis of joint disorders by segmentation of ultrasound image with different algorithms. Ultrasound machine with high resolution probe can be used for development & findings of joints by the orthopaedician, rheumatologist and sports physician. These find-ings are done by processing the ultrasound images of patient joint using modern image processing techniques. Therefore algorithms has been designed and developed for analysis of medical images that is musculo ultrasound image based on optimization approach, using genet-ic algorithm and PSO algorithm. To improve the better quality of the image many improvisation techniques have been introduced. Hence, these algorithms perform better segmentation and identification of joint abnormalities. The analysis of ultrasound image is directly based on image segmentation steps, pre-processing, filtering, feature extraction and analysis of these extracted features by finding the output using different optimization techniques. In proposed method, efforts have been made to exhibit the procedure for finding and segmenting the mus-culoskeletal images of abnormal joints. The present approaches are segmentation operation on ultrasound images by applying genetic and PSO algorithm. The comparison between these algorithms is done, such that the algorithm itself analyses the whole image and perform the segmentation and detection of abnormalities perfectly

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Kwak, Deawon, Jiwoo Choi, and Sungjin Lee. "Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition." Sensors 23, no. 4 (February 19, 2023): 2307. http://dx.doi.org/10.3390/s23042307.

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This paper explored techniques for diagnosing breast cancer using deep learning based medical image recognition. X-ray (Mammography) images, ultrasound images, and histopathology images are used to improve the accuracy of the process by diagnosing breast cancer classification and by inferring their affected location. For this goal, the image recognition application strategies for the maximal diagnosis accuracy in each medical image data are investigated in terms of various image classification (VGGNet19, ResNet50, DenseNet121, EfficietNet v2), image segmentation (UNet, ResUNet++, DeepLab v3), and related loss functions (binary cross entropy, dice Loss, Tversky loss), and data augmentation. As a result of evaluations through the presented methods, when using filter-based data augmentation, ResNet50 showed the best performance in image classification, and UNet showed the best performance in both X-ray image and ultrasound image as image segmentation. When applying the proposed image recognition strategies for the maximal diagnosis accuracy in each medical image data, the accuracy can be improved by 33.3% in image segmentation in X-ray images, 29.9% in image segmentation in ultrasound images, and 22.8% in image classification in histopathology images.

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Bao, Junxiao, Cuilin Bei, Xiang Zheng, and Jinli Wang. "Deep Learning Algorithm in Biomedical Engineering in Intelligent Automatic Processing and Analysis of Sports Images." Wireless Communications and Mobile Computing 2022 (July 30, 2022): 1–10. http://dx.doi.org/10.1155/2022/3196491.

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In order to improve the detection and identification ability of sports injury ultrasound medicine, a segmentation method of sports injury ultrasound medical image based on local features is proposed, and the research on the sports injury ultrasound medical detection and identification ability is carried out. Methods of the sports injury ultrasound medical image segmentation model are established; the sports injury ultrasound medical image information is enhanced by using the sports skeletal muscle block matching technology; the image features are extracted; and the characteristics of sports injury ultrasound medical images are analyzed by CT bright spot feature transmission. In detail, combined with the deep convolutional neural network training method, the extracted sports injury points are automatically detected for sports injury ultrasound medical images, and the sports injury ultrasound medical image segmentation is realized. The simulation results show that this method has high accuracy for sports injury ultrasound medical image segmentation, the error value can be controlled below 0.103, and finally, the effect of zero error is achieved. It is confirmed that the method proposed in this paper has high resolution and accuracy for sports injury point detection and has strong practical application ability.

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Sree, S. Jayanthi, and C. Vasanthanayaki. "Ultrasound Fetal Image Segmentation Techniques: A Review." Current Medical Imaging Formerly Current Medical Imaging Reviews 15, no. 1 (December 7, 2018): 52–60. http://dx.doi.org/10.2174/1573405613666170622115527.

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Background: This paper reviews segmentation techniques for 2D ultrasound fetal images. Fetal anatomy measurements derived from the segmentation results are used to monitor the growth of the fetus. </P><P> Discussion: The segmentation of fetal ultrasound images is a difficult task due to inherent artifacts and degradation of image quality with gestational age. There are segmentation techniques for particular biological structures such as head, stomach, and femur. The whole fetal segmentation algorithms are only very few. Conclusion: This paper presents a review of these segmentation techniques and the metrics used to evaluate them are summarized.

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Shao, Liping, Zubang Zhou, Hongmei Wu, Jinrong Ni, and Shulan Li. "Modeling of Hidden Markov in Ultrasound Image-Assisted Diagnosis." Journal of Healthcare Engineering 2021 (April 12, 2021): 1–10. http://dx.doi.org/10.1155/2021/5597591.

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Different segmentation of lung nodules using the same segmentation algorithm can easily lead to excessive segmentation errors. Therefore, it is necessary to design an effective segmentation algorithm to improve image segmentation accuracy. Based on the hidden Markov model, this study processed the ultrasound images of pulmonary nodules to improve their diagnostic results. At the same time, this study was combined with the ultrasound image of lung nodules to process the ultrasound images. In addition, this study combines the convex hull algorithm for image processing, uses the improved vector method to repair, improves image recognizability, establishes a reliable feature extraction algorithm, and establishes a comprehensive diagnostic model. Finally, this study designed the test for performance analysis. Through experimental research, it can be seen that the model constructed in this study has certain clinical effects and can provide theoretical reference for subsequent related research.

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Wu, Shibin, Shaode Yu, Ling Zhuang, Xinhua Wei, Mark Sak, Neb Duric, Jiani Hu, and Yaoqin Xie. "Automatic Segmentation of Ultrasound Tomography Image." BioMed Research International 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/2059036.

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Ultrasound tomography (UST) image segmentation is fundamental in breast density estimation, medicine response analysis, and anatomical change quantification. Existing methods are time consuming and require massive manual interaction. To address these issues, an automatic algorithm based on GrabCut (AUGC) is proposed in this paper. The presented method designs automated GrabCut initialization for incomplete labeling and is sped up with multicore parallel programming. To verify performance, AUGC is applied to segment thirty-two in vivo UST volumetric images. The performance of AUGC is validated with breast overlapping metrics (Dice coefficient (D), Jaccard (J), and False positive (FP)) and time cost (TC). Furthermore, AUGC is compared to other methods, including Confidence Connected Region Growing (CCRG), watershed, and Active Contour based Curve Delineation (ACCD). Experimental results indicate that AUGC achieves the highest accuracy (D=0.9275 and J=0.8660 and FP=0.0077) and takes on average about 4 seconds to process a volumetric image. It was said that AUGC benefits large-scale studies by using UST images for breast cancer screening and pathological quantification.

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Noble, J. A., and D. Boukerroui. "Ultrasound image segmentation: a survey." IEEE Transactions on Medical Imaging 25, no. 8 (August 2006): 987–1010. http://dx.doi.org/10.1109/tmi.2006.877092.

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Sun, Jingmeng, and Yifei Liu. "Segmentation for Human Motion Injury Ultrasound Medical Images Using Deep Feature Fusion." Mathematical Problems in Engineering 2022 (August 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/4825720.

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Image processing technology assists physicians in the analysis of athletes’ human motion injuries, not only to improve the accuracy of athletes’ injury detection but also to improve the localization and recognition of injury locations. It is important to accurately segment human motion injury ultrasound medical images. To address many problems such as poor effect of traditional ultrasonic medical image segmentation algorithm for a sports injury. Therefore, we propose a segmentation algorithm for human motion injury ultrasound medical images using deep feature fusion. First, the accurate estimated value of human posture is extracted and combined with image texture features and image gray value as the target feature value of the ultrasonic medical image of human motion injury. Second, the image features are deeply fused by an adaptive fusion algorithm to enhance the image resolution. Finally, the best segmentation value of the image is obtained by the trained support vector machine to realize the accurate segmentation of human motion injury ultrasonic medical image. The results show that the average accuracy of the posture accurate estimation of the proposed algorithm is 95.97%; the segmentation time of the human motion injury ultrasound medical image of the proposed algorithm is below 150 ms; and the convergence of the algorithm is completed when the number of iterations is 3. The maximum segmentation error rate is 2.68%. The image segmentation effect is consistent with the ideal target segmentation effect. The proposed algorithm has important application value in the field of ultrasonic medical diagnosis of sports injury.

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Suri, Jasjit, Yujun Guo, Cara Coad, Tim Danielson, Idris Elbakri, and Roman Janer. "Image Quality Assessment via Segmentation of Breast Lesion in X-ray and Ultrasound Phantom Images from Fischer's Full Field Digital Mammography and Ultrasound (FFDMUS) System." Technology in Cancer Research & Treatment 4, no. 1 (February 2005): 83–92. http://dx.doi.org/10.1177/153303460500400111.

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Fischer has been developing a fused full-field digital mammography and ultrasound (FFDMUS) system funded by the National Institute of Health (NIH). In FFDMUS, two sets of acquisitions are performed: 2-D X-ray and 3-D ultrasound. The segmentation of acquired lesions in phantom images is important: (i) to assess the image quality of X-ray and ultrasound images; (ii) to register multi-modality images; and (iii) to establish an automatic lesion detection methodology to assist the radiologist. In this paper we developed lesion segmentation strategies for ultrasound and X-ray images acquired using FFDMUS. For ultrasound lesion segmentation, a signal-to-noise (SNR)-based method was adapted. For X-ray segmentation, we used gradient vector flow (GVF)-based deformable model. The performance of these segmentation algorithms was evaluated. We also performed partial volume correction (PVC) analysis on the segmentation of ultrasound images. For X-ray lesion segmentation, we also studied the effect of PDE smoothing on GVF's ability to segment the lesion. We conclude that ultrasound image qualities from FFDMUS and Hand-Held ultrasound (HHUS) are comparable. The mean percentage error with PVC was 4.56% (4.31%) and 6.63% (5.89%) for 5 mm lesion and 3 mm lesion respectively. The mean average error from the segmented X-ray images with PDE yielded an average error of 9.61%. We also tested our program on synthetic datasets. The system was developed for Linux workstation using C/C++.

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Cai, Lina, Qingkai Li, Junhua Zhang, Zhenghua Zhang, Rui Yang, and Lun Zhang. "Ultrasound image segmentation based on Transformer and U-Net with joint loss." PeerJ Computer Science 9 (October 20, 2023): e1638. http://dx.doi.org/10.7717/peerj-cs.1638.

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Background Ultrasound image segmentation is challenging due to the low signal-to-noise ratio and poor quality of ultrasound images. With deep learning advancements, convolutional neural networks (CNNs) have been widely used for ultrasound image segmentation. However, due to the intrinsic locality of convolutional operations and the varying shapes of segmentation objects, segmentation methods based on CNNs still face challenges with accuracy and generalization. In addition, Transformer is a network architecture with self-attention mechanisms that performs well in the field of computer vision. Based on the characteristics of Transformer and CNNs, we propose a hybrid architecture based on Transformer and U-Net with joint loss for ultrasound image segmentation, referred to as TU-Net. Methods TU-Net is based on the encoder-decoder architecture and includes encoder, parallel attention mechanism and decoder modules. The encoder module is responsible for reducing dimensions and capturing different levels of feature information from ultrasound images; the parallel attention mechanism is responsible for capturing global and multiscale local feature information; and the decoder module is responsible for gradually recovering dimensions and delineating the boundaries of the segmentation target. Additionally, we adopt joint loss to optimize learning and improve segmentation accuracy. We use experiments on datasets of two types of ultrasound images to verify the proposed architecture. We use the Dice scores, precision, recall, Hausdorff distance (HD) and average symmetric surface distance (ASD) as evaluation metrics for segmentation performance. Results For the brachia plexus and fetal head ultrasound image datasets, TU-Net achieves mean Dice scores of 79.59% and 97.94%; precisions of 81.25% and 98.18%; recalls of 80.19% and 97.72%; HDs (mm) of 12.44 and 6.93; and ASDs (mm) of 4.29 and 2.97, respectively. Compared with those of the other six segmentation algorithms, the mean values of TU-Net increased by approximately 3.41%, 2.62%, 3.74%, 36.40% and 31.96% for the Dice score, precision, recall, HD and ASD, respectively.

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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 evaluation
El 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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Books on the topic "Ultrasound image segmentation"

1

wasson, vikas, and gurinder kaur. Novel Approach for Thyroid Segmentation of Ultrasound Images Based on Neural Networks. Independently Published, 2018.

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Evaluation of Segmentation for Bone Structures in 3D Rendering of Ultrasound Residual Limb Images. Storming Media, 1996.

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Book chapters on the topic "Ultrasound image segmentation"

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Stojanovski, David, Uxio Hermida, Pablo Lamata, Arian Beqiri, and Alberto Gomez. "Echo from Noise: Synthetic Ultrasound Image Generation Using Diffusion Models for Real Image Segmentation." In Simplifying Medical Ultrasound, 34–43. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44521-7_4.

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AbstractWe propose a novel pipeline for the generation of synthetic ultrasound images via Denoising Diffusion Probabilistic Models (DDPMs) guided by cardiac semantic label maps. We show that these synthetic images can serve as a viable substitute for real data in the training of deep-learning models for ultrasound image analysis tasks such as cardiac segmentation. To demonstrate the effectiveness of this approach, we generated synthetic 2D echocardiograms and trained a neural network for segmenting the left ventricle and left atrium. The performance of the network trained on exclusively synthetic images was evaluated on an unseen dataset of real images and yielded mean Dice scores of $$88.6 \pm 4.91$$ 88.6 ± 4.91 , $$91.9 \pm 4.22$$ 91.9 ± 4.22 , $$85.2 \pm 4.83$$ 85.2 ± 4.83 % for left ventricular endocardium, epicardium and left atrial segmentation respectively. This represents a relative increase of 9.2, 3.3 and 13.9% in Dice scores compared to the previous state-of-the-art. The proposed pipeline has potential for application to a wide range of other tasks across various medical imaging modalities.

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Kolář, Radim, and Jiří Kozumplík. "Fuzzy Approach in Ultrasound Image Segmentation." In Computational Intelligence. Theory and Applications, 924–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45493-4_92.

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Li, Haoming, Xin Yang, Jiamin Liang, Wenlong Shi, Chaoyu Chen, Haoran Dou, Rui Li, et al. "Contrastive Rendering for Ultrasound Image Segmentation." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 563–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59716-0_54.

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Archip, Neculai, Robert Rohling, Peter Cooperberg, Hamid Tahmasebpour, and Simon K. Warfield. "Spectral Clustering Algorithms for Ultrasound Image Segmentation." In Lecture Notes in Computer Science, 862–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11566489_106.

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Talebi, Mohammad, and Ahmad Ayatollahi. "Genetic Snake for Medical Ultrasound Image Segmentation." In Lecture Notes in Computer Science, 48–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21596-4_6.

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Casaburi, D., L. D’Amore, L. Marcellino, and A. Murli. "A Motion-Aided Ultrasound Image Sequence Segmentation." In Numerical Mathematics and Advanced Applications 2009, 217–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11795-4_22.

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Shah, Nemil, Jay Bhatia, Nimit Vasavat, Kanishk Shah, and Pratik B. Kanani. "Ultrasound Nerve Image Segmentation Using Attention Mechanism." In Lecture Notes in Electrical Engineering, 789–802. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5974-7_63.

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Yang, Xin, Haoran Dou, Ran Li, Xu Wang, Cheng Bian, Shengli Li, Dong Ni, and Pheng-Ann Heng. "Generalizing Deep Models for Ultrasound Image Segmentation." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 497–505. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00937-3_57.

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Li, W., J. G. Bosch, Y. Zhong, H. v. Urk, E. J. Gussenhoven, F. Mastik, F. v. Egmond, H. Rijsterborgh, J. H. C. Reiber, and N. Bom. "Image Segmentation and 3D Reconstruction of Intravascular Ultrasound Images." In Acoustical Imaging, 489–96. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-2958-3_65.

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Yang, Xin, Lequan Yu, Shengli Li, Xu Wang, Na Wang, Jing Qin, Dong Ni, and Pheng-Ann Heng. "Towards Automatic Semantic Segmentation in Volumetric Ultrasound." In Medical Image Computing and Computer Assisted Intervention − MICCAI 2017, 711–19. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66182-7_81.

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Conference papers on the topic "Ultrasound image segmentation"

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Nugroho, Anan, Risanuri Hidayat, and Hanung Adi Nugroho. "Thyroid Ultrasound Image Segmentation: A Review." In 2019 5th International Conference on Science and Technology (ICST). IEEE, 2019. http://dx.doi.org/10.1109/icst47872.2019.9166443.

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Bass, Vivian, Julieta Mateos, Ivan M. Rosado-Mendez, and Jorge Márquez. "Ultrasound image segmentation methods: A review." In PROCEEDINGS OF THE XVI MEXICAN SYMPOSIUM ON MEDICAL PHYSICS. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0051110.

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Yuan, Baichuan, Yoni Dukler, Long Zhao, Yizhou Qian, Yurun Ge, Shintaro Yamamoto, Blake Hunter, Andrea L. Bertozzi, Jesse T. Yen, and Rafael Llerena. "Automatic valve segmentation in cardiac ultrasound time series data." In Image Processing, edited by Elsa D. Angelini and Bennett A. Landman. SPIE, 2018. http://dx.doi.org/10.1117/12.2293255.

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Triyani, Yuli, Hanung Adi Nugroho, Made Rahmawaty, Igi Ardiyanto, and Lina Choridah. "Performance analysis of image segmentation for breast ultrasound images." In 2016 8th International Conference on Information Technology and Electrical Engineering (ICITEE). IEEE, 2016. http://dx.doi.org/10.1109/iciteed.2016.7863298.

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Sridevi, S., and M. Sundaresan. "Survey of image segmentation algorithms on ultrasound medical images." In 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering (PRIME). IEEE, 2013. http://dx.doi.org/10.1109/icprime.2013.6496475.

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Kissi, A., S. Cormier, L. Pourcelot, and F. Tranquart. "Automatic lesions segmentation in ultrasound nonlinear imaging." In 2005 International Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1529960.

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Liu, Qi, Yinan Ge, Yue Ou, and Biao Cao. "Freehand 3D ultrasound breast tumor segmentation." In International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Jianguo Liu, Kunio Doi, Patrick S. P. Wang, and Qiang Li. SPIE, 2007. http://dx.doi.org/10.1117/12.743708.

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de Carvalho, Isabela Miller, Rodrigo Leite Q. Basto, Antonio Fernando C. Infantosi, Marco Antonio von Kruger, and Wagner Coelho de A. Pereira. "Breast ultrasound phantom for image segmentation assessment." In 2009 IEEE International Ultrasonics Symposium. IEEE, 2009. http://dx.doi.org/10.1109/ultsym.2009.5441417.

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Karunanayake, Nalan, and Stanislav S. Makhanov. "Artificial Life for Breast Ultrasound Image Segmentation." In 2022 7th International Conference on Frontiers of Signal Processing (ICFSP). IEEE, 2022. http://dx.doi.org/10.1109/icfsp55781.2022.9924946.

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Agarwalla, Rhythm, Satyajeet Kumar Ray, and Saurav Paul. "AttentionFractalCovNet Architecture for Breast Ultrasound Image Segmentation." In 2023 OITS International Conference on Information Technology (OCIT). IEEE, 2023. http://dx.doi.org/10.1109/ocit59427.2023.10430724.

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Reports on the topic "Ultrasound image segmentation"

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He, Ping, and Jun Zheng. Segmentation of TIBIA Bone in Ultrasound Images Using Active Shape Models. Fort Belvoir, VA: Defense Technical Information Center, October 2001. http://dx.doi.org/10.21236/ada412425.

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