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Carr, Jonathan. "Surface reconstruction in 3D medical imaging." Thesis, University of Canterbury. Electrical Engineering, 1996. http://hdl.handle.net/10092/6533.
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This thesis addresses two problems in medical imaging, the development of a system for 3D imaging with ultrasound and a system for making titanium prostheses for cranioplasty. Central to both problems is the construction and depiction of surfaces from volume data where the data is not acquired on a regular grid or is incomplete.
A system for acquiring 3D pulse-echo ultrasound data using a conventional 2D ultrasound scanner equipped with an electro-magnetic spatial locator is described. The non-parallel nature of 2D B-scan slices acquired by the system requires the development of new visualisation algorithms to depict three dimensional structures. Two methods for visualising iso-valued surfaces from the ultrasound data are presented. One forms an intermediate volume reconstruction suitable for conventional ray-casting while the second method renders surfaces directly from the slice data. In vivo imaging of human anatomy is used to demonstrate reconstructions of tissue surfaces. Filtering and spatial compounding of scan data is used to reduce speckle. The manifestation of 2D artefacts in 3D surface reconstructions is also illustrated.
Pulse-echo ultrasound primarily depicts tissue boundaries. These are characterised by incomplete acoustic interfaces contaminated by noise. The problem of reconstructing tissue interfaces from ultrasound data is viewed as an example of the general problem of reconstructing an object's shape from unorganised surface data. A novel method for reconstructing surfaces in the absence of a priori knowledge of the object's shape, is described and applied to 3D ultrasound data. The method uses projections through the surface data taken from many viewpoints to reconstruct surfaces. Aspects of the method are similar to work in computer vision concerning the determination of the shape of 3D objects from their silhouettes. This work is extended significantly in this thesis by considering the reconstruction of incomplete objects in the presence of noise and through the development of practical algorithms for pixel and voxel data. Furthermore, the reconstruction of realistic, non-convex objects is considered rather than simple geometric objects. 2D and 3D ultrasound data derived from phantoms, as well as artificial data, are used to demonstrate reconstructions.
The second problem studied in this thesis concerns designing cranial implants to repair defects in the skull. Skull surfaces are extracted from X-ray CT data by ray-casting iso-valued surfaces. A tensor product B-spline interpolant is used in the ray-caster to reduce ripples in the surface data due to partial voluming and the large spacing between CT slices. The associated surface depth-maps are characterised by large irregular holes which correspond to the defect regions requiring repair. Defects are graphically identified by a user in surface-rendered images. Radial basis function approximation is introduced as a method of interpolating the surface of the skull across these defect regions. The fitted surface is used to produce CNC milling instructions to machine a mould in the shape of the surface from a block of hard plastic resin. A cranial implant is then formed by pressing flat titanium plate into the mould under high pressure in a hydraulic press. The system improves upon current treatment procedures by avoiding the manual aspects of fashioning an implant. It is also suitable when other techniques which use symmetry to reconstruct the skull are inadequate or not possible. The system has been successfully used to treat patients at Christchurch Hospital. Radial basis function (RBF) approximation has previously been restricted to problems where the number of interpolation centres is small. The use of newly developed fast methods for evaluating radial basis interpolants in the surface interpolation software results in a computationally efficient system for designing cranial implants and demonstrates that RBFs are potentially of wide interest in medical imaging and engineering problems where data does not lie on a regular grid.
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Jones, Jonathan-Lee. "2D and 3D segmentation of medical images." Thesis, Swansea University, 2015. https://cronfa.swan.ac.uk/Record/cronfa42504.
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Cardiovascular disease is one of the leading causes of the morbidity and mortality in the western world today. Many different imaging modalities are in place today to diagnose and investigate cardiovascular diseases. Each of these, however, has strengths and weaknesses. There are different forms of noise and artifacts in each image modality that combine to make the field of medical image analysis both important and challenging. The aim of this thesis is develop a reliable method for segmentation of vessel structures in medical imaging, combining the expert knowledge of the user in such a way as to maintain efficiency whilst overcoming the inherent noise and artifacts present in the images. We present results from 2D segmentation techniques using different methodologies, before developing 3D techniques for segmenting vessel shape from a series of images. The main drive of the work involves the investigation of medical images obtained using catheter based techniques, namely Intra Vascular Ultrasound (IVUS) and Optical Coherence Tomography (OCT). We will present a robust segmentation paradigm, combining both edge and region information to segment the media-adventitia, and lumenal borders in those modalities respectively. By using a semi-interactive method that utilizes "soft" constraints, allowing imprecise user input which provides a balance between using the user's expert knowledge and efficiency. In the later part of the work, we develop automatic methods for segmenting the walls of lymph vessels. These methods are employed on sequential images in order to obtain data to reconstruct the vessel walls in the region of the lymph valves. We investigated methods to segment the vessel walls both individually and simultaneously, and compared the results both quantitatively and qualitatively in order obtain the most appropriate for the 3D reconstruction of the vessel wall. Lastly, we adapt the semi-interactive method used on vessels earlier into 3D to help segment out the lymph valve. This involved the user interactive method to provide guidance to help segment the boundary of the lymph vessel, then we apply a minimal surface segmentation methodology to provide segmentation of the valve.
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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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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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Eljaaidi, Abdalla Agila. "2D & 3D ultrasound systems in development of medical imaging technology." Thesis, Cape Peninsula University of Technology, 2016. http://hdl.handle.net/20.500.11838/2193.
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Thesis (MTech (Electrical Engineering))--Cape Peninsula University of Technology, 2016.Ultrasound is widely used in most medical clinics, especially obstetrical clinics. It is a way of imaging methods that has important diagnostic value. Although useful in many different applications, diagnostic ultrasound is especially useful in antenatal (before delivery) diagnosis. The use of two-dimensional ultrasound (2DUS) in obstetrics has been established. However, there are many disadvantages of 2DUS imaging. Several researchers have published information on the significance of patients being shown the ultrasound screen during examination, especially during three- and four-dimensional (3D/4D) scanning. In addition, a form of ultrasound, called keepsake or entertainment ultrasound, has boomed, particularly in the United States. However, long-term epidemiological studies have failed to show the adverse effects of ultrasound in human tissues. Until now, there is no proof that diagnostic ultrasound causes harm in a human body or the developing foetus when used correctly. While ultrasound is supposed to be absolutely safe, it is a form of energy and, as such, has effects on tissues it traverses (bio-effects). The two most important mechanisms for effects are thermal and non-thermal. These two mechanisms are indicated on the screen of ultrasound devices by two indices: The thermal index (TI) and the mechanical index (MI). These are the purposes of this thesis: • evaluate end-users’ knowledge regarding the safety of ultrasound; • evaluate and make a comparison between acoustic output indices (AOI) in B-mode (2D) and three-dimensional (3D) ultrasound – those measured by thermal (TI) and mechanical (MI) indices; • assess the acoustic output indices (AOI) to benchmark current practice with a survey conducted by the British Medical Ultrasound Society (BMUS); and • review how to design 2D and 3D arrays for medical ultrasound imaging
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Law, Kwok-wai Albert, and 羅國偉. "3D reconstruction of coronary artery and brain tumor from 2D medical images." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B31245572.
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Badawi, Ramsey Derek. "Aspects of optimisation and qualification in 3D positron emission tomography." Thesis, King's College London (University of London), 1998. https://kclpure.kcl.ac.uk/portal/en/theses/aspects-of-optimisation-and-qualification-in-3d-positron-emission-tomography(47a88023-9d6c-453f-aa8d-fcc5b83ae168).html.
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Pellegrini, Giulio. "Technology development of 3D detectors for high energy physics and medical imaging." Thesis, University of Glasgow, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269510.
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Rathod, Gaurav Dilip. "An improved effective method for generating 3D printable models from medical imaging." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/80415.
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Medical practitioners rely heavily on visualization of medical imaging to get a better understanding of the patient's anatomy. Most cancer treatment and surgery today are performed using medical imaging. Medical imaging is therefore of great importance to the medical industry.
Medical imaging continues to depend heavily on a series of 2D scans, resulting in a series of 2D photographs being displayed using light boxes and/or computer monitors. Today, these 2D images are increasingly combined into 3D solid models using software. These 3D models can be used for improved visualization and understanding of the problem at hand, including fabricating physical 3D models using additive manufacturing technologies.
Generating precise 3D solid models automatically from 2D scans is non-trivial. Geometric and/or topologic errors are common, and often costly manual editing is required to produce 3D solid models that sufficiently reflect the actual underlying human geometry. These errors arise from the ambiguity of converting from 2D data to 3D data, and also from inherent limitations of the .STL fileformat used in additive manufacturing.
This thesis proposes a new, robust method for automatically generating 3D models from 2D scanned data (e.g., computed tomography (CT) or magnetic resonance imaging (MRI)), where the resulting 3D solid models are specifically generated for use with additive manufacturing. This new method does not rely on complicated procedures such as contour evolution and geometric spline generation, but uses volume reconstruction instead. The advantage of this approach is that the original scan data values are kept intact longer, so that the resulting surface is more accurate. This new method is demonstrated using medical CT data of the human nasal airway system, resulting in physical 3D models fabricated via additive manufacturing.Master of Science
10
Li, Jianchun. "Design of an FPGA-based computing platform for realtime 3D medical imaging." online version, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=case1106098912.
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Numburi, Uma D. "3D Imaging for Planning of Minimally Invasive Surgical Procedures." Cleveland State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=csu1308704453.
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Chen, Kailiang. "A Column-Row-Parallel ASIC architecture for 3D wearable / portable medical ultrasonic imaging." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/87916.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 163-174).
This work presents a scalable Column-Row-Parallel ASIC architecture for 3D wearable / portable medical ultrasound. It leverages programmable electronic addressing to achieve linear scaling for both hardware interconnection and software data acquisition. A 16x16 transceiver ASIC is fabricated and flip-chip bonded to a 16x16 capacitive micromachined ultrasonic transducer (CMUT) to demonstrate the compact, low-power front-end assembly. A 3D plane-wave coherent compounding algorithm is designed for fast volume rate (62.5 volume/s), high quality 3D ultrasonic imaging. An interleaved checker board pattern with I&Q excitations is also proposed for ultrasonic harmonic imaging, reducing transmitted second harmonic distortion by over 20dB, applicable to nonlinear transducers and circuits with arbitrary pulse shapes. Each transceiver circuit is element-matched to its CMUT element. The high voltage transmitter employs a 3-level pulse-shaping technique with charge recycling to enhance the power efficiency, requiring minimum off-chip components. Compared to traditional 2-level pulsers, 50% more acoustic power delivery is obtained with the same total power dissipation. The receiver is implemented with a transimpedance amplifier topology and achieves a lowest noise efficiency factor in the literature (2.1 compared to a previously reported lowest of 3.6, in unit of mPa - [square root sign]mW/Hz). A source follower stage is specially designed to combine the analog outputs of receivers in parallel, improving output SNR as parallelization increases and offering flexibility for imaging algorithm design. Lastly, fault-tolerance is incorporated into the transceiver to deal with faulty elements within the 2D MEMS transducer array, increasing yield for the system assembly.
by Kailiang Chen.
Ph. D.
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Li, Jianchun. "DESIGN OF AN FPGA-BASED COMPUTING PLATFORM FOR REAL-TIME 3D MEDICAL IMAGING." Case Western Reserve University School of Graduate Studies / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=case1106098912.
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Poon, Miranda. "3D livewire and live-vessel : minimal path methods for interactive medical image segmentation." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2736.
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Medical image analysis is a ubiquitous and essential part of modem health care. A
crucial first step to this is segmentation, which is often complicated by many factors
including subject diversity, pathology, noise corruption, and poor image resolution.
Traditionally, manual tracing by experts was done. While considered accurate, this
process is time consuming and tedious, especially when performed slice-by-slice on
three-dimensional (3D) images over large datasets or on two-dimensional (2D) but
topologically complicated images such as a retinography. On the other hand, fully-automated
methods are typically faster, but work best with data-dependent, carefully
tuned parameters and still require user validation and refinement.
This thesis contributes to the field of medical image segmentation by proposing a
highly-automated, interactive approach that effectively merges user knowledge and
efficient computing. To this end, our work focuses on graph-based methods and offer
globally optimal solutions. First, we present a novel method for 3D segmentation based
on a 3D Livewire approach. This approach is an extension of the 2D Livewire
framework, and this method is capable of handling objects with large protrusions,
concavities, branching, and complex arbitrary topologies. Second, we propose a method
for efficiently segmenting 2D vascular networks, called ‘Live-Vessel’. Live-Vessel
simultaneously extracts vessel centrelines and boundary points, and globally optimizes
over both spatial variables and vessel radius. Both of our proposed methods are validated
on synthetic data, real medical data, and are shown to be highly reproducible, accurate,
and efficient. Also, they were shown to be resilient to high amounts of noise and
insensitive to internal parameterization.
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Lakhotia, Kritika. "Visualization and quantification of 3D tumor-host interface architecture reconstructed from digital histopathology slides." Thesis, State University of New York at Buffalo, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10127616.
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Oral cavity cancer (OCC) is a type of cancer of the lip, tongue, salivary glands and other sites in the mouth (buccal or oral cavity) and is the sixth leading cause of cancer worldwide. Patients with OCC are treated based on a staging system: low-stage patients typically receive less aggressive therapy compared to high-stage patients. Unfortunately, low-stage patients are sometimes at risk for locoregional recurrence. Recently, a semi-quantitative risk scoring system has been developed to assess the locoregional recurrence risk for low-stage patients. This risk scoring system is based on tissue characteristics determined on 2D histopathology images under a microscope. This modality limits the appreciation of the 3D architecture of the tumor and its associated morphological features. This thesis aims to visualize 3D models of the tumor-host interface reconstructed from serially-sectioned histopathology slides and quantify their clinically validated morphological features to predict locoregional recurrence after treatment. The 3D models are developed and quantified for 6 patient cases using readily available tools. This pilot study provides a framework for an automated diagnostic technique for 3D visualization and morphological analysis of tumor biology which is traditionally done using 2D analysis.
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Al, Zu'bi Shadi Mahmoud. "3D multiresolution statistical approaches for accelerated medical image and volume segmentation." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/5300.
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Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques together or on their own to segment medical volumes, the system takes a stack of 2D slices or a full 3D volumes acquired from medical scanners as a data input. Two main approaches have been implemented in this research for segmenting medical volume which are multi-resolution analysis and statistical modeling. Multi-resolution analysis has been mainly employed in this research for extracting the features. Higher dimensions of discontinuity (line or curve singularity) have been extracted in medical images using a modified multi-resolution analysis transforms such as ridgelet and curvelet transforms. The second implemented approach in this thesis is the use of statistical modeling in medical image segmentation; Hidden Markov models have been enhanced here to segment medical slices automatically, accurately, reliably and with lossless results. But the problem with using Markov models here is the computational time which is too long. This has been addressed by using feature reduction techniques which has also been implemented in this thesis. Some feature reduction and dimensionality reduction techniques have been used to accelerate the slowest block in the proposed system. This includes Principle Components Analysis, Gaussian Pyramids and other methods. The feature reduction techniques have been employed efficiently with the 3D volume segmentation techniques such as 3D wavelet and 3D Hidden Markov models. The system has been tested and validated using several procedures starting at a comparison with the predefined results, crossing the specialists’ validations, and ending by validating the system using a survey filled by the end users explaining the techniques and the results. This concludes that Markovian models segmentation results has overcome all other techniques in most patients’ cases. Curvelet transform has been also proved promising segmentation results; the end users rate it better than Markovian models due to the long time required with Hidden Markov models.
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Essafi, Salma. "3D Knowledge-based Segmentation Using Sparse Hierarchical Models : contribution and Applications in Medical Imaging." Phd thesis, Ecole Centrale Paris, 2010. http://tel.archives-ouvertes.fr/tel-00534805.
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CETTE thèse est consacrée à la conception d'un système d'aide au diagnostic dédiéau muscle squelettique humain. Au cours du premier volet de ce manuscrit nousproposons une nouvelle représentation basée sur les modèles parcimonieux dans le cadrede la segmentation d'Images de Résonances Magnétiques (IRM) T1 du muscle squelettiquedu mollet. Notre méthode Sparse Shape Model/ Modèle de Formes Parcimonieux(MFP), apprend un modèle statistique de formes et de textures locales annoté et réussità en tirer une représentation réduite afin de reconstruire le mécanisme musculaire sur unexemple test. Dans la seconde partie du manuscrit, nous présentons une approche baséesur des ondelettes de diffusion pour la segmentation du muscle squelettique. Contrairementaux méthodes de l'état de l'art, notre approche au cours de la phase d'apprentissagepermet à optimiser les coefficients des ondelettes, ainsi que leur nombres et leur positions.Le modèle prend en charge aussi bien les hiérarchies dans l'espace de recherche,que l'encodage des dépendances géométriques complexes et photométriques de la structured'intérêt. Notre modélisation offre ainsi l'avantage de traiter des topologies arbitraires.L'évaluation expérimentale a été effectué sur un ensemble de mollets acquisespar un scanner IRM, ainsi qu'un ensemble d'images tomodensitométriques du ventriculegauche.
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Yang, Mu. "RadPaint a Web-based interactive 3D virtual radiation field application /." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE1001198.
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Phelps, Emma. "Exploring patients' experience of viewing their own 3D medical imaging results during a clinical consultation." Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/90841/.
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Background: Patients can struggle to comprehend and recall medical information, hindering their ability to participate in their own care. Research suggests that images may aid comprehension of medical information. Available for use in clinical practice, 3D medical images are relatively easy to interpret and could benefit lay people. However, little is known about patients’ experience of viewing them. Aim: The aim was to understand the role of a patient’s own 3D image in a clinical consultation. Four objectives were explored, to: (i) understand the impact for patients viewing their 3D image; (ii) understand how 3D images are incorporated into consultations; (iii) compare the experience of viewing 3D images, 2D images and no image alongside a diagnosis and (iv) understand whether informing participants of the occurrence of errors within image interpretation affects their trust in a diagnosis. Methods: A multi-method approach was adopted. Fourteen patients and four clinicians from a tertiary care orthopaedic outpatient clinic participated in semi-structured interviews and 10 clinical consultations were video-recorded. Additionally, 31 volunteers participated in focus groups and 252 volunteers participated in psychology laboratory experiments. Results: Patients considered their 3D images to be evidence, describing them to be truthful and authoritative. 3D images were used to explain diagnoses and treatments to patients during consultations. Participants showed better recall of the diagnosis when it was accompanied by 3D and 2D images compared to no image. Additionally, participants reported greater understanding and trust when the diagnosis was accompanied by 3D images compared to 2D images or no image. There was no significant difference in trust between participants who were informed of the potential for error within image interpretation and those who were not. Conclusion: Patients trust 3D images, perceiving them to provide authoritative knowledge. They may be a powerful resource for patients, increasing patient understanding, trust, and recall.
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Bertrand, Arnaud. "Mise en place de l'imagerie Cerenkov 3D." Thesis, Strasbourg, 2015. http://www.theses.fr/2015STRAE020/document.
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L’imagerie moléculaire vise à étudier les processus biologiques in vivo. L’imagerie Cerenkov est une technique d’imagerie moléculaire qui se développe depuis 2009. Le principe est d’injecter un radiotraceur, molécule marquée par un isotope radioactif, puis à enregistrer le signal optique émis par effet Cerenkov. L’imagerie Cerenkov permet d’imager des radiotraceurs émettant des rayonnements β+ (positon) et β- (électron).L’effet Cerenkov se produit lorsqu’une particule chargée se déplace dans un milieu avec une vitesse supérieure à celle de la lumière dans ce même milieu. Si ce seuil est dépassé, on observe alors une émission de photons optiques appelée rayonnement Cerenkov. Le spectre de cette émission s’étend de l’UV à l’IR de manière continue et le nombre de photons émis en fonction de la longueur d’onde varie en 1/λ².Mon thèse consiste à développer l’imagerie Cerenkov 3D pour reconstruire la distribution du radiotraceur in vivo. Nous disposons d’une plateforme d’imagerie nommée AMISSA (A Multimodality Imaging System for Small Animal) dont le but est de développer et de mettre à disposition des outils d’imagerie moléculaire pour du petit animalMolecular imaging aims to study biological processes in vivo. Cerenkov imaging is a molecular imaging technology that has developed since 2009. The principle is to inject a radioactive tracer molecule labeled with a radioactive isotope, then recording the optical signal emitted by the Cerenkov effect. The Cerenkov imaging allows imaging radiotracers emitting β+ radiation (positron) and β- (electron). The Cerenkov effect occurs when a charged particle moves through a medium with a speed greater than that of light in this same medium. If this threshold is exceeded, we observed an emission of optical photons called Cerenkov radiation. The emission spectrum of this extends from UV to IR continuously and the number of photons emitted as a function of the wavelength varies by 1/λ². My PhD is to develop 3D imaging Cerenkov to reconstruct the distribution of the radiotracer in vivo. We have an imaging platform named Amissa (A Multimodality Imaging System for Small Animal) whose purpose is to develop and make available tools for molecular imaging of small animals
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Forbes, Jessica LeeAnn. "Development and verification of medical image analysis tools within the 3D slicer environment." Thesis, University of Iowa, 2016. https://ir.uiowa.edu/etd/3085.
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Rapid development of domain specialized medical imaging tools is essential for deploying medical imaging technologies to advance clinical research and clinical practice. This work describes the development process, deployment method, and evaluation of modules constructed within the 3D Slicer environment. These tools address critical problems encountered in four different clinical domains: quality control review of large repositories of medical images, rule-based automated label map cleaning, quantification of calcification in the heart using low-dose radiation scanning, and waist circumference measurement from abdominal scans. Each of these modules enables and accelerates clinical research by incorporating medical imaging technologies that minimize manual human effort. They are distributed within the multi-platform 3D Slicer Extension Manager environment for use in the computational environment most convenient to the clinician scientist.
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Kadia, Dhaval Dilip. "Advanced UNet for 3D Lung Segmentation and Applications." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619440426233034.
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Ioannou, Christos. "Fetal skeletal imaging using 3D ultrasound and the impact of maternal vitamin D." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:cf8d5030-a117-4548-921c-e802c873c40f.
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Background: Previous research suggests that vitamin D deficiency during pregnancy may be associated with suboptimal fetal growth, but direct evidence is lacking. Our objectives were 1) to develop a method for measurement of the fetal sphenoidal fontanelle area (FA) and femur volume (FV) using 3D ultrasound; 2) to create normal charts for FA and FV; and 3) to correlate FA and FV with maternal vitamin D concentration. Methods: FA measurement in 3D was evaluated in vitro and in vivo. Different segmentation methods for FV measurement were explored. A novel FV method was described which consists of three linear measurements and a volume equation; this was validated in vitro and also by comparing FV measured sonographically to the true volume assessed by computed tomography (CT), in 6 cases following pregnancy termination. A cohort of 868 uncomplicated pregnancies was selected on the basis of strict inclusion criteria; participants underwent serial ultrasound scans for FV and multilevel modeling was used for the creation of a “prescriptive” FV chart. Finally, a different cohort of 357 healthy pregnant women had serum vitamin D levels and FV ultrasound at 34 weeks gestation and dual emission x-ray absorptiometry (DEXA) of their neonates in order to investigate the prenatal determinants of fetal bone mass. Results: FA measurement was accurate in vitro, but unreliable in vivo and was therefore abandoned. A novel FV method had excellent agreement with CT and superior repeatability compared with segmentation-based methods. A normal FV chart was created and the regression equations for the median and percentile values were presented. Vitamin D demonstrated a significant correlation with FV. Conclusions: FV is a reliable sonographic marker of skeletal growth. Maternal vitamin D deficiency is associated with reduced FV. This finding has public health implications as reduced bone mass may increase the lifetime risk of osteoporosis, through fetal programming.
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Favretto, Fernanda Oliveira. "Registro de imagens 3D do cerebro humano." [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/276178.
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Orientador: Alexandre Xavier FalcãoDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
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Resumo: O registro de imagens é o processo que alinha duas ou mais imagens em um mesmo sistema de coordenadas espaciais [31]. Na área de Imagens Médicas, o problema de registro de imagens tem muitas aplicações permitindo, por exemplo, a análise da variação de fenômenos e estruturas anatômicas ao longo do tempo, pelo registro de imagens de uma mesma modalidade obtidas em diferentes instantes de tempo; ou o estudo das informações anatômicas e fisiológicas combinadas para uma dada estrutura fenômeno, pelo registro de imagens obtidas por modalidades diferentes. O objetivo deste trabalho é o desenvolvimento de uma técnica de registro para imagens tridimensionais do cérebro humano, cuja motivação é o estudo comparativo de imagens de Ressonância Magnética pré- e pós-operatórias do cérebro de pacientes de epilepsia. Um estudo recente [80] tem observado que nos casos em que houve crises recorrentes, após a remoção cirúrgica do foco da crise, os pacientes apresentaram alterações nas substâncias cinza e branca do cérebro. O registro das imagens pré- e pós- operatórias desses pacientes permite a análise dessas alterações. Foi desenvolvida uma técnica de registro rígido que realiza o alinhamento de imagens 3D de forma automática, rápida e precisa. O método baseia-se no casamento das linhas de watershed marcador de cinza extraídas da imagem móvel com uma imagem de borda realçada pelo gradiente morfológico da imagem fixa. A busca dos parâmetros de rotação e translação que compõem a função de mapeamento é feita através de uma técnica proposta neste trabalho, denominada Descendente de Gradiente em Múltiplas Escalas (MSGD) - um variante do tradicional método de Descendente de Gradiente - a qual permite passos de tamanhos escalonados dos vetores de gradiente, evitando mínimos locais indesejáveis e convergindo para o ótimo desejado mais rapidamente. O método foi avaliado em imagens 3D de ressonância magnética do cérebro humano ponderadas em T1 e obteve bons resultados. Os experimentos envolveram 2 bases de dados. A primeira base é a base de dados de controle, composta por 200 pares de imagens, onde o registro foi realizado em aproxidamente 45s e obteve erro médio de rotação de 0, 06?, 0, 08? e 0, 08? com desvio padrão de 0, 06, 0, 25 e 0, 08 nos eixos X, Y e Z, respectivamente, e erro médio de translação de 1, 67mm, 1, 55mm e 2, 27mm com desvio padrão de 1, 83, 1, 45 e 2, 27 nos eixos X, Y e Z, respectivamente. A segunda base foi uma base de dados clínicos, composta por imagens pré- e pós-operatórias de pacientes com epilepsia, que comprovou a eficácia do método em dados clínicos reais. Também foram desenvolvidas duas técnicas de visualização do registro, uma delas baseada no mosaico das imagens registradas e a outra que combina as imagens em um único volume colorido, onde as alterações de tecidos são identificadas pelas cores vermelha e verde. Portanto, as principais contribuições deste trabalho são: uma metodologia para o registro, que envolve combinação eficiente de características, métrica de similaridade e estratégia de busca; a estratégia MSGD que se mostrou promissora para outros problemas de otimização; e uma técnica de visualização das imagens registradas na forma de um volume colorido.
Abstract: Image Registration is the process that aligns two or more images in a common reference system of spacial coordinates [31]. It is an important problem with several applications in Medical Imaging, enabling, for instance, the analysis of changes in anatomy along time by the registration of images from the same modality, and the study of combined anatomic and physiologic data by the registration of images from different modalities. The objective of this work is the development of a registration method for 3D images of the human brain, and the motivation is a comparative study of pre and post-surgical images from epilepsy patients. A recent study [80] has observed that some pacients, who did not cease the seizures after surgery, presented variations in their brain tissues. The registration of pre and post-surgical images enables the analysis of these tissue's variations. We developed a rigid registration method that aligns 3D images in a fast, automatic and accurate way. The method is based on the matching between watershed lines extrated from a source image and a morphological gradient image from the target image. The search for the parameters of rotation and translation that compose the mapping function is done by a techinique proposed in this work, named Multi-Scale Gradient Descent - a variant of the tradicional method Gradient Descent - which enables gradient's vectors with scaled magnitudes, avoiding undesirable local minima and fastly converging to the desired optimum. The method was evaluated on 3D T1-weighted Magnetic Ressonance Images of the human brain. The experiments used 2 data bases: a control data base, composed by 200 pairs of images, in which the method took approximately 45s and acceptable results; and a data base of patients, composed by pre- and post-surgical images, demonstrating the effectiveness of the method for real data. We have also developed visualization techiniques for the registred images: the checkerboard image, that alternates the target and registered source in a checkerboard pattern, allowing the user to inspect the correctness, coherence and continuity of the registration; and the colorized image, that combines the target and registered source images in a single colorized volume, such that the alterations of the tissues can be identified by the red and green colors. Therefore, the main contributions of this work are: a 3D registration methodology, that involves an effective combination of feature selection, similarity measure and search strategy; a search strategy, MSGD, that seems to be promissing for other optimization problems; and a visualization techinique that uses a colorized volume to combine the registered images.
Mestrado
Processamento e Analise de Imagens
Mestre em Ciência da Computação
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Fan, Li. "3D reconstruction and deformation analysis from medical image sequences with applications in left ventricle and lung /." free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9999280.
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Nguyen, Peter. "CANNABINOID RECEPTORS IN THE 3D RECONSTRUCTED MOUSE BRAIN: FUNCTION AND REGULATION." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/2274.
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CB1 receptors (CB1R) mediate the psychoactive and therapeutic effects of cannabinoids including ∆9-tetrahydrocannabinol (THC), the main psychoactive constituent in marijuana. However, therapeutic use is limited by side effects and tolerance and dependence with chronic administration. Tolerance to cannabinoid-mediated effects is associated with CB1R adaptations, including desensitization (receptor-G-protein uncoupling) and downregulation (receptor degradation). The objectives of this thesis are to investigate the regional-specificity in CB1R function and regulation. Previous studies have investigated CB1Rs in a subset of regions involved in cannabinoid effects, but an inclusive regional comparison of the relative efficacies of different classes of cannabinoids to activate G-proteins has not been conducted. A novel unbiased whole-brain analysis was developed based on Statistical Parametric Mapping (SPM) for 3D-reconstructed mouse brain images derived from agonist-stimulated [35S]GTPgS autoradiography, which has not been described before. SPM demonstrated regional differences in the relative efficacies of cannabinoid agonists methanandamide (M-AEA), CP55,940 (CP), and WIN55,212-2 (WIN) in mouse brains. To assess potential contribution of novel sites, CB1R knockout (KO) mice were used. SPM analysis revealed that WIN, but not CP or M-AEA, stimulated [35S]GTPgS binding in regions that partially overlapped with the expression of CB1Rs. We then examined the role of the regulatory protein Beta-arrestin-2 (βarr2) in CB1R adaptations to chronic THC treatment. Deletion of βarr2 reduced CB1R desensitization/downregulation in the cerebellum, caudal periaqueductal gray (PAG), and spinal cord. However in hippocampus, amygdala and rostral PAG, similar desensitization was present in both genotypes. Interestingly, enhanced desensitization was found in the hypothalamus and cortex in βarr2 KO animals. Intra-regional differences in the magnitude of desensitization were noted in the caudal hippocampus, where βarr2 KO animals exhibited greater desensitization compared to WT. Regional differences in βarr2-mediated CB1R adaptation were associated with differential effects on tolerance, where THC-mediated antinociception, but not catalepsy or hypothermia, was attenuated in βarr2 KO mice. Overall, studies using SPM revealed intra- and inter-regional specificity in the function and regulation of CB1Rs and underscores an advantage of using a whole-brain unbiased approach. Understanding the regulation of CB1R signaling within different anatomical contexts represents an important fundamental prerequisite in the therapeutic exploitation of the cannabinoid system.
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Prabhu, David. "Automated Plaque Characterization of Intravascular Optical Coherence Tomography (IVOCT) Images Using 3D Cryo-image/Histology Validation." Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1556293860943414.
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Sanchez, Silva Victor F. "Advances in medical image compression : novel schemes for highly efficient storage, transmission and on demand scalable access for 3D and 4D medical imaging data." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/27281.
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Three dimensional (3D) and four dimensional (4D) medical images are increasingly being used in many clinical and research applications. Due to their huge file size, 3D and 4D medical images pose heavy demands on storage and archiving resources. Lossless compression methods usually facilitate the access and reduce the storage burden of such data, while avoiding any loss of valuable clinical data. In this thesis, we propose novel methods for highly efficient storage and scalable access of 3D and 4D medical imaging data that outperform the state-of the-art. Specifically, we propose (1) a symmetry-based technique for scalable lossless compression of 3D medical images; (2) a 3D scalable medical image compression method with optimized volume of interest (VOI) coding; (3) a motion-compensation-based technique for lossless compression of 4D medical images; and (4) a lossless functional magnetic resonance imaging (fMRI) compression method based on motion compensation and customized entropy coding.
The proposed symmetry-based technique for scalable lossless compression of 3D medical images employs wavelet transform technology and a prediction method to reduce the energy of the wavelet sub-bands based on a set of axes of symmetry. We achieve VOI coding by employing an optimization technique that maximizes reconstruction quality of a VOI at any bit-rate, while incorporating partial background information and allowing for gradual increase in peripheral quality around the VOI.
The proposed lossless compression method for 4D medical imaging data employs motion compensation and estimation to exploit the spatial and temporal correlations of 4D medical images. Similarly, the proposed fMRI lossless compression method employs a motion compensation process that uses a 4D search, bi-directional prediction and variable-size block matching for motion estimation; and a new context-based adaptive binary arithmetic coder to compress the residual and motion vector data generated by the motion compensation process.
We demonstrate that the proposed methods achieve a superior compression performance compared to the state-of-the-art, including JPEG2000 and 3D-JPEG2000.
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D'Souza, Aswin Cletus. "Automated counting of cell bodies using Nissl stained cross-sectional images." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-2035.
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Cheng, Jiqi. "A Study of Wave Propagation and Limited-Diffraction Beams for Medical Imaging." University of Toledo / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1133820434.
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Bruce, Michael P. "Detection of Endoscopic Looping During Colonoscopy Procedure Using Embedded Bending Sensors." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1429796708.
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Keenan, Bethany Elin. "Medical imaging and Biomechanical analysis of scoliosis progression in the growing adolescent spine." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/84532/1/Bethany%20Elin_Keenan_Thesis.pdf.
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Progression of spinal deformity in children was studied with Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) to identify how gravity affects the deformity and to determine the full three-dimensional character of the deformity. The CT study showed that gravity is significant in deformity progression in some patients which has implications for clinical patient management. The world first MRI study showed that the standard clinical measure used to define the extent of the deformity is inadequate and further use of three-dimensional MRI should be considered by spinal surgeons.
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Yu, Boliang. "3D analysis of bone ultra structure from phase nano-CT imaging." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI016/document.
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L'objectif de cette thèse était de quantifier le réseau lacuno-canaliculaire du tissu osseux à partir d’images 3D acquises en nano CT synchrotron de phase. Ceci a nécessité d’optimiser les processus d’acquisition et de reconstruction de phase, ainsi que de développer des méthodes efficaces de traitement d'images pour la segmentation et l’analyse 3D. Dans un premier temps, nous avons étudié et évalué différents algorithmes de reconstruction de phase. Nous avons étendu la méthode de Paganin pour plusieurs distances de propagation et l’avons évaluée et comparée à d’autres méthodes, théoriquement puis sur nos données expérimentales Nous avons développé une chaine d’analyse, incluant la segmentation des images et prenant en compte les gros volumes de données à traiter. Pour la segmentation des lacunes, nous avons choisi des méthodes telles que le filtre médian, le seuillage par hystérésis et l'analyse par composantes connexes. La segmentation des canalicules repose sur une méthode de croissance de région après rehaussement des structures tubulaires. Nous avons calculé des paramètres de porosité, des descripteurs morphologiques des lacunes ainsi que des nombres de canalicules par lacune. Par ailleurs, nous avons introduit des notions de paramètres locaux calculés dans le voisinage des lacunes. Nous avons obtenu des résultats sur des images acquises à différentes tailles de voxel (120nm, 50nm, 30nm) et avons également pu étudier l’impact de la taille de voxel sur les résultats. Finalement ces méthodes ont été utilisées pour analyser un ensemble de 27 échantillons acquis à 100 nm dans le cadre du projet ANR MULTIPS. Nous avons pu réaliser une analyse statistique pour étudier les différences liées au sexe et à l'âge. Nos travaux apportent de nouvelles données quantitatives sur le tissu osseux qui devraient contribuer à la recherche sur les mécanismes de fragilité osseuse en relation avec des maladies comme l’ostéoporoseOsteoporosis is a bone fragility disease resulting in abnormalities in bone mass and density. In order to prevent osteoporotic fractures, it is important to have a better understanding of the processes involved in fracture at various scales. As the most abundant bone cells, osteocytes may act as orchestrators of bone remodeling which regulate the activities of both osteoclasts and osteoblasts. The osteocyte system is deeply embedded inside the bone matrix and also called lacuno-canalicular network (LCN). Although several imaging techniques have recently been proposed, the 3D observation and analysis of the LCN at high spatial resolution is still challenging. The aim of this work was to investigate and analyze the LCN in human cortical bone in three dimensions with an isotropic spatial resolution using magnified X-ray phase nano-CT. We performed image acquisition at different voxel sizes of 120 nm, 100 nm, 50 nm and 30 nm in the beamlines ID16A and ID16B of the European Synchrotron Radiation Facility (ESRF - European Synchrotron Radiation Facility - Grenoble). Our first study concerned phase retrieval, which is the first step of data processing and consists in solving a non-linear inverse problem. We proposed an extension of Paganin’s method suited to multi-distance acquisitions, which has been used to retrieve phase maps in our experiments. The method was compared theoretically and experimentally to the contrast transfer function (CTF) approach for homogeneous object. The analysis of the 3D reconstructed images requires first to segment the LCN, including both the segmentation of lacunae and of canaliculi. We developed a workflow based on median filter, hysteresis thresholding and morphology filters to segment lacunae. Concerning the segmentation of canaliculi, we made use of the vesselness enhancement to improve the visibility of line structures, the variational region growing to extract canaliculi and connected components analysis to remove residual noise. For the quantitative assessment of the LCN, we calculated morphological descriptors based on an automatic and efficient 3D analysis method developed in our group. For the lacunae, we calculated some parameters like the number of lacunae, the bone volume, the total volume of all lacunae, the lacunar volume density, the average lacunae volume, the average lacunae surface, the average length, width and depth of lacunae. For the canaliculi, we first computed the total volume of all the canaliculi and canalicular volume density. Moreover, we counted the number of canaliculi at different distances from the surface of each lacuna by an automatic method, which could be used to evaluate the ramification of canaliculi. We reported the statistical results obtained on the different groups and at different spatial resolutions, providing unique information about the organization of the LCN in human bone in three dimensions
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Nöjdh, Oscar. "Intelligent boundary extraction for area and volume measurement : Using LiveWire for 2D and 3D contour extraction in medical imaging." Thesis, Linköpings universitet, Programvara och system, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-136448.
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This thesis tries to answer if a semi-automatic tool can speed up the process of segmenting tumors to find the area of a slice in the tumor or the volume of the entire tumor. A few different 2D semi-automatic tools were considered. The final choice was to implement live-wire. The implemented live-wire was evaluated and improved upon with hands-on testing from developers. Two methods were found for extending live-wire to 3D bodies. The first method was to interpolate the seed points and create new contours using the new seed points. The second method was to let the user segment contours in two orthogonal projections. The intersections between those contours and planes in the third orthogonal projection were then used to create automatic contours in this third projection. Both tools were implemented and evaluated. The evaluation compared the two tools to manual segmentation on two cases posing different difficulties. Time-on-task and accuracy were measured during the evaluation. The evaluation revealed that the semi-automatic tools could indeed save the user time while maintaining acceptable (80%) accuracy. The significance of all results were analyzed using two-tailed t-tests.
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Zhao, Yue. "Biopsy needles localization and tracking methods in 3d medical ultrasound with ROI-RANSAC-KALMAN." Thesis, Lyon, INSA, 2014. http://www.theses.fr/2014ISAL0015/document.
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Dans les examens médicaux et les actes de thérapie, les techniques minimalement invasives sont de plus en plus utilisées. Des instruments comme des aiguilles de biopsie, ou des électrodes sont utilisés pour extraire des échantillons de cellules ou pour effectuer des traitements. Afin de réduire les traumatismes et de faciliter le suivi visuelle de ces interventions, des systèmes d’assistance par imagerie médicale, comme par exemple, par l’échographie 2D, sont utilisés dans la procédure chirurgicale. Nous proposons d’utiliser l’échographie 3D pour faciliter la visualisation de l’aiguille, mais en raison de l’aspect bruité de l’image ultrasonore (US) et la grande quantité de données d’un volume 3D, il est difficile de trouver l’aiguille de biopsie avec précision et de suivre sa position en temps réel. Afin de résoudre les deux principaux problèmes ci-dessus, nous avons proposé une méthode basée sur un algorithme RANSAC et un filtre de Kalman. De même l’étude est limitée à une région d’intérêt (ROI) pour obtenir une localisation robuste et le suivi de la position de l’aiguille de biopsie en temps réel. La méthode ROI-RK se compose de deux étapes: l’étape d’initialisation et l’étape de suivi. Dans la première étape, une stratégie d’initialisation d’une ROI en utilisant le filtrage de ligne à base de matrice de Hesse est mise en œuvre. Cette étape permet de réduire efficacement le bruit de granularité du volume US, et de renforcer les structures linéaires telles que des aiguilles de biopsie. Dans la deuxième étape, après l’initialisation de la ROI, un cycle de suivi commence. L’algorithme RK localise et suit l’aiguille de biopsie dans une situation dynamique. L’algorithme RANSAC est utilisé pour estimer la position des micro-outils et le filtrage de Kalman permet de mettre à jour la région d’intérêt et de corriger la localisation de l’aiguille. Une stratégie d’estimation de mouvement est également appliquée pour estimer la vitesse d’insertion de l’aiguille de biopsie. Des volumes 3D US avec un fond inhomogène ont été simulés pour vérifier les performances de la méthode ROI-RK. La méthode a été testée dans des conditions variables, telles que l’orientation d’insertion de l’aiguille par rapport à l’axe de la sonde et le niveau de contraste (CR). La précision de la localisation est de moins de 1 mm, quelle que soit la direction d’insertion de l’aiguille. Ce n’est que lorsque le CR est très faible que la méthode proposée peut échouer dans le suivi d’une structure incomplète de l’aiguille. Une autre méthode, utilisant l’algorithme RANSAC avec apprentissage automatique a été proposée. Cette méthode vise à classer les voxels en se basant non seulement sur l’intensité, mais aussi sur les caractéristiques de la structure de l’aiguille de biopsie. Les résultats des simulations montrent que l’algorithme RANSAC avec apprentissage automatique peut séparer les voxels de l’aiguille et les voxels de tissu de fond avec un CR faibleIn medical examinations and surgeries, minimally invasive technologies are getting used more and more often. Some specially designed surgical instruments, like biopsy needles, or electrodes are operated by radiologists or robotic systems and inserted in human’s body for extracting cell samples or delivering radiation therapy. To reduce the risk of tissue injury and facilitate the visual tracking, some medical vision assistance systems, as for example, ultrasound (US) systems can be used during the surgical procedure. We have proposed to use the 3D US to facilitate the visualization of the biopsy needle, however, due to the strong speckle noise of US images and the large calculation load involved as soon as 3D data are involved, it is a challenge to locate the biopsy needle accurately and to track its position in real time in 3D US. In order to solve the two main problems above, we propose a method based on the RANSAC algorithm and Kalman filter. In this method, a region of interest (ROI) has been limited to robustly localize and track the position of the biopsy needle in real time. The ROI-RK method consists of two steps: the initialization step and the tracking step. In the first step, a ROI initialization strategy using Hessian based line filter measurement is implemented. This step can efficiently reduce the speckle noise of the ultrasound volume, and enhance line-like structures as biopsy needles. In the second step, after the ROI is initialized, a tracking loop begins. The RK algorithm can robustly localize and track the biopsy needles in a dynamic situation. The RANSAC algorithm is used to estimate the position of the micro-tools and the Kalman filter helps to update the ROI and auto-correct the needle localization result. Because the ROI-RK method is involved in a dynamic situation, a motion estimation strategy is also implemented to estimate the insertion speed of the biopsy needle. 3D US volumes with inhomogeneous background have been simulated to evaluate the performance of the ROI-RK method. The method has been tested under different conditions, such as insertion orientations angles, and contrast ratio (CR). The localization accuracy is within 1 mm no matter what the insertion direction is. Only when the CR is very low, the proposed method could fail to track because of an incomplete ultrasound imaging of the needle. Another methodology, i.e. RANSAC with machine learning (ML) algorithm has been presented. This method aims at classifying the voxels not only depending on their intensities, but also using some structure features of the biopsy needle. The simulation results show that the RANSAC with ML algorithm can separate the needle voxels and background tissue voxels with low CR
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Vaďura, Jiří. "Využití Vertex a Pixel shaderu v OpenGL pro 3D zobrazení 3D obrazových dat v medicíně." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236625.
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This thesis deals with accelerated 3D rendering of medical data, e.g. computed tomography, using a graphics processor and OpenGL library. Raw data slices are send to graphic memory and rendered by a ray-casting algorithm. The goal of this project is high quality visual output and full user interaction at the same time. Multiple rendering modes are avaiable to the user: MIP, X-Ray simulation and realistic shading.
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Dolet, Aneline. "2D and 3D multispectral photoacoustic imaging - Application to the evaluation of blood oxygen concentration." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSEI070/document.
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L'imagerie photoacoustique est une modalité d'imagerie fonctionnelle basée sur la génération d'ondes acoustiques par des tissus soumis à une illumination optique (impulsion laser). L'utilisation de différentes longueurs d'ondes optiques permet la discrimination des milieux imagés. Cette modalité est prometteuse pour de nombreuses applications médicales liées, par exemple, à la croissance, au vieillissement et à l'évolution de la vascularisation des tissus. En effet, l'accès à l'oxygénation du sang dans les tissus est rendu possible par l'imagerie photoacoustique. Cela permet, entre autres applications, la discrimination de tumeurs bénignes ou malignes et la datation de la mort tissulaire (nécrose). Ce travail de thèse a pour objectif principal la construction d'une chaîne de traitement des données photoacoustiques multispectrales pour le calcul de l'oxygénation du sang dans les tissus. Les principales étapes sont, d'une part, la discrimination des données (clustering), pour extraire les zones d'intérêt, et d'autre part, la quantification des différents constituants présents dans celles-ci (unmixing). Plusieurs méthodes non supervisées de discrimination et de quantification ont été développées et leurs performances comparées sur des données photoacoustiques multispectrales expérimentales. Celles-ci ont été acquises sur la plateforme photoacoustique du laboratoire, lors de collaborations avec d'autres laboratoires et également sur un système commercial. Pour la validation des méthodes développées, de nombreux fantômes contenant différents absorbeurs optiques ont été conçus. Lors du séjour de cotutelle de thèse en Italie, des modes d'imagerie spécifiques pour l'imagerie photoacoustique 2D et 3D temps-réel ont été développés sur un échographe de recherche. Enfin, des acquisitions in vivo sur modèle animal (souris) au moyen d'un système commercial ont été réalisées pour valider ces développementsPhotoacoustic imaging is a functional technique based on the creation of acoustic waves from tissues excited by an optical source (laser pulses). The illumination of a region of interest, with a range of optical wavelengths, allows the discrimination of the imaged media. This modality is promising for various medical applications in which growth, aging and evolution of tissue vascularization have to be studied. Thereby, photoacoustic imaging provides access to blood oxygenation in biological tissues and also allows the discrimination of benign or malignant tumors and the dating of tissue death (necrosis). The present thesis aims at developing a multispectral photoacoustic image processing chain for the calculation of blood oxygenation in biological tissues. The main steps are, first, the data discrimination (clustering), to extract the regions of interest, and second, the quantification of the different media in these regions (unmixing). Several unsupervised clustering and unmixing methods have been developed and their performance compared on experimental multispectral photoacoustic data. They were acquired on the experimental photoacoustic platform of the laboratory, during collaborations with other laboratories and also on a commercial system. For the validation of the developed methods, many phantoms containing different optical absorbers have been produced. During the co-supervision stay in Italy, specific imaging modes for 2D and 3D real-time photoacoustic imaging were developed on a research scanner. Finally, in vivo acquisitions using a commercial system were conducted on animal model (mouse) to validate these developments
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Palmberg, Staffan, and Magnus Ranlöf. "A Collaborative VolumeViewer." Thesis, Linköping University, Department of Science and Technology, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1153.
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This study has been carried out as a part of the EC funded project, SMARTDOC IST-2000-28137, with the objective of developing application components that provide highly interactive visualization and collaboration functionalities. The low-level components from the graphics library AVS OpenViz 2.0 are used as the development basis. The application components can be inserted into electronic documents that allow embedded controls such as web documents or Microsoft Word or PowerPoint documents. Instead of displaying results as static images, a SMARTDOC component provides the ability to visualize data and interact with it inside the document.
Although the principal goal of the SMARTDOC project is to create components in a number of different application domains this study concentrates on developing a medical imaging application component in collaboration with the project partners AETmed and professor Alan Jackson at the University of Manchester. By incorporating the application component into patient reports, the clinicians are provided the ability to interact with the 3D data that is described in the reports. To improve the usability of the component, it makes use of a visual user interface (VUI), which gives the user the ability to interact and change parameters directly in the visualization process.
Collaborative work over geographical distances is an area that is becoming increasingly common and thus more interesting. As the availability of bandwidth has increased and the communication technologies have advanced, many companies express their interest for this new practical method of work. A company with offices in different countries would benefit from collaborative techniques providing closer cooperation within the company. Specialized institutions and laboratories could gather much experience and information through collaborative research. Medical imaging and visualization technique are areas where distinct disciplines such as networking, user interfaces and 3D visualization naturally can be fused together in order to develop collaborative environments. The visualization components developed within the SMARTDOC project will be the foundation for collaborative application components integrated with the Microsoft DirectX® multimedia library. In the medical imaging area, collaborative work can be used to improve diagnoses, journaling and teaching.
This study focuses on developing a prototype of an interactive visualization component for 3D medical imaging and creating a collaborative environment using a multimedia library originally meant for network gaming.
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Chen, Zhiang. "Deep-learning Approaches to Object Recognition from 3D Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1496303868914492.
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Song, Xin. "Path reconstruction in diffusion tensor magnetic resonance imaging." Phd thesis, INSA de Lyon, 2011. http://tel.archives-ouvertes.fr/tel-00694403.
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The complicated underwater environment and the poor underwater vision make super-mini underwater cable robot hardly to be controlled. Traditionally, the manual control method by operators is adopted by this kind of robots. Unfortunately, the robots can hardly work normally in these practical circumstances. Therefore, to overcome these shortcomings and improve the abilities of these underwater cable robots, this paper proposes several improvements, including the system design, the motion controller design, three dimensional obstacle recognition and three dimensional path reconstruction technologies etc. The details are displayed as follow: (1) Super-mini underwater robot system design: several improvement schemes and important design ideas are investigated for the super-mini underwater robot.(2) Super-mini robot motion controller design: The motion controller design of underwater robot in complicated circumstance is investigated. A new adaptive neural network sliding mode controller with balanced parameter controller (ANNSMB) is proposed. Based on the theory of adaptive fuzzy sliding mode controller (AFSMC), an improved algorithm is also proposed and applied to the underwater robot. (3)Research of three dimensional underwater environment reconstructions: The algorithms and the experiments of underwater environment reconstructions are investigated. DT-MRI image processing algorithm and the theory of three dimensional obstacle reconstructions are adopted and improved for the application of the underwater robot. (4) The super-mini underwater robot path planning algorithms are investigated.
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Orhun, Koray. "Interactive Volume Rendering For Medical Images." Thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605542/index.pdf.
Full textAbstract:
Volume rendering is one of the branches of scientific visualization. Its popularity has grown in the recent years, and due to the increase in the computation speed of the graphics hardware of the desktop systems, became more and more accessible. Visualizing volumetric datasets using volume rendering technique requires a large amount of trilinear interpolation operations that are computationally expensive. This situation used to restrict volume rendering methods to be used only in high-end graphics workstations or with special-purpose hardware. In this thesis, an application tool has been developed using hardware accelerated volume rendering techniques on commercial graphics processing devices. This implementation has been developed with a 3D texture based approach using bump mapping for building an illumination model with OpenGL API. The aim of this work is to propose visualization methods and tools for rendering medical image datasets at interactive rates. The methods and tool are validated and compared with a commercially available software.
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Christopoulos, Charitos Andreas. "Brain disease classification using multi-channel 3D convolutional neural networks." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-174329.
Full textAbstract:
Functional magnetic resonance imaging (fMRI) technology has been used in the investigation of human brain functionality and assist in brain disease diagnosis. While fMRI can be used to model both spatial and temporal brain functionality, the analysis of the fMRI images and the discovery of patterns for certain brain diseases is still a challenging task in medical imaging. Deep learning has been used more and more in medical field in an effort to further improve disease diagnosis due to its effectiveness in discovering high-level features in images. Convolutional neural networks (CNNs) is a class of deep learning algorithm that have been successfully used in medical imaging and extract spatial hierarchical features. The application of CNNs in fMRI and the extraction of brain functional patterns is an open field for research. This project focuses on how fMRIs can be used to improve Autism Spectrum Disorders (ASD) detection and diagnosis with 3D resting-state functional MRI (rs-fMRI) images. ASDs are a range of neurodevelopment brain diseases that mostly affect social function. Some of the symptoms include social and communicating difficulties, and also restricted and repetitive behaviors. The symptoms appear on early childhood and tend to develop in time thus an early diagnosis is required. Finding a proper model for identifying between ASD and healthy subject is a challenging task and involves a lot of hyper-parameter tuning. In this project a grid search approach is followed in the quest of the optimal CNN architecture. Additionally, regularization and augmentation techniques are implemented in an effort to further improve the models performance.
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Pereañez, Marco. "Enlargement, subdivision and individualization of statistical shape models: Application to 3D medical image segmentation." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/441754.
Full textAbstract:
This thesis presents three original and complementary approaches to enhance the quality
of Statistical Shape Models (SSMs), that improve the accuracy of medical image
segmentation in challenging applications. First, we enhance the statistical richness of SSMs
by developing a technique capable of merging the shape representations and statistical
properties of several pre-existing models with no original or additional raw data. Second,
we enhance the geometrical quality of SSMs by developing a framework for modeling
simultaneously both global and local characteristics of highly complex and/or multi-part
anatomical shapes. Last, we improve the specificity of SSMs for specific subjects by
integrating individual-specific non-imaging metadata such as demographic, clinical and
behavioral variables into the SSM construction and image segmentation tasks. These
techniques are demonstrated and validated by considering various imaging modalities such
as magnetic resonance imaging (MRI) and computed tomography (CT), and different
complex anatomies, including the human heart, brain and spine.Esta tesis presenta tres propuestas originales y complementarias para mejorar la calidad de los modelos estadísticos de formas (SSMs) que mejoran la precisión de la segmentación de la imagen médica en aplicaciones difíciles. Proponemos, primero, mejorar la riqueza estadística de los SSMs por medio de una técnica para unir la representación de forma y las propiedades estadísticas de muchos modelos pre-existentes sin observaciones adicionales. Segundo, mejorar la representacion geométrica de los SSMs modelando simultáneamente las características globales y locales del objecto o de multiples anatomias. Por último, mejorar la especificidad de los SSMs mediante la integración de metadatos del paciente no derivados de la imagen, tales como, variables demográficas, conductuales y de entorno clínico, en la construcción de los modelos. Estas técnicas son demostradas y validadas en imágenes de resonancia magnética (MRI) y tomografía computarizada (CT) y en anatomias como el corazón, el cerebro y la espina dorsal humanos.
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Ovreiu, Elena. "Accurate 3D mesh simplification." Phd thesis, INSA de Lyon, 2012. http://tel.archives-ouvertes.fr/tel-00838783.
Full textAbstract:
Complex 3D digital objects are used in many domains such as animation films, scientific visualization, medical imaging and computer vision. These objects are usually represented by triangular meshes with many triangles. The simplification of those objects in order to keep them as close as possible to the original has received a lot of attention in the recent years. In this context, we propose a simplification algorithm which is focused on the accuracy of the simplifications. The mesh simplification uses edges collapses with vertex relocation by minimizing an error metric. Accuracy is obtained with the two error metrics we use: the Accurate Measure of Quadratic Error (AMQE) and the Symmetric Measure of Quadratic Error (SMQE). AMQE is computed as the weighted sum of squared distances between the simplified mesh and the original one. Accuracy of the measure of the geometric deviation introduced in the mesh by an edge collapse is given by the distances between surfaces. The distances are computed in between sample points of the simplified mesh and the faces of the original one. SMQE is similar to the AMQE method but computed in the both, direct and reverse directions, i.e. simplified to original and original to simplified meshes. The SMQE approach is computationnaly more expensive than the AMQE but the advantage of computing the AMQE in a reverse fashion results in the preservation of boundaries, sharp features and isolated regions of the mesh. For both measures we obtain better results than methods proposed in the literature.
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Montagnat, Johan. "Modèles déformables pour la segmentation et la modélisation d'images médicales 3D et 4D." Phd thesis, Université de Nice Sophia-Antipolis, 1999. http://tel.archives-ouvertes.fr/tel-00683368.
Full textAbstract:
Dans cette thèse, nous nous intéressons à l'utilisation des modèles déformables surfaciques pour la segmentation d'images 3D et 4D. Dans un premier temps, nous nous sommes attachés à contraindre l'espace des déformations admissibles du modèle afin de rendre le processus de déformation plus fiable. Nous avons utilisé le formalisme des maillages simplexes pour exprimer des contraintes régularisantes de la surface. Nous avons développé un processus évolutif de déformation combinant une transformation globale ayant peu de degrés de liberté et un champ de déformations locales. Il permet de contrôler le nombre de degrés de liberté offerts au modèle surfacique de manière simple et efficace. Nous avons également cherché à enrichir la connaissance a priori des données apportée par le modèle. Nous utilisons des contraintes de forme pour faciliter la segmentation des structures à reconstruire, notamment dans les zones où les données de l'image sont bruitées ou lacunaires. Nous nous sommes également intéressés à la convergence formelle du processus de déformation. Nous avons développé un algorithme de changement de topologie des modèles discrets que nous avons comparé à l'approche par ensembles de niveaux. Dans un deuxième temps, nous nous sommes intéressés à la définition du terme d'attache aux données pour différents types d'images 3D. Nous avons envisagé plusieurs géométries rencontrées dans les images médicales. Nous avons étudié l'apport d'une information sur les régions ou sur la distribution des niveaux de gris de l'image pour la déformation ou le recalage multimodal d'un modèle. Finalement, nous nous sommes intéressés à la segmentation de séquences temporelles d'images cardiaques 2D ou 3D. La prise en compte de l'information temporelle permet d'introduire de nouvelles contraintes de déformations. Nous avons mis nos méthodes en pratique avec la segmentation d'images ou des séquences d'images cardiaques provenant de différentes modalités d'acquisition.
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Lorintiu, Oana. "Reconstruction par acquisition compressée en imagerie ultrasonore médicale 3D et Doppler." Thesis, Lyon, INSA, 2015. http://www.theses.fr/2015ISAL0093/document.
Full textAbstract:
DL’objectif de cette thèse est le développement de techniques adaptées à l’application de la théorie de l’acquisition compressée en imagerie ultrasonore 3D et Doppler. En imagerie ultrasonore 3D une des principales difficultés concerne le temps d’acquisition très long lié au nombre de lignes RF à acquérir pour couvrir l’ensemble du volume. Afin d’augmenter la cadence d’imagerie une solution possible consiste à choisir aléatoirement des lignes RF qui ne seront pas acquises. La reconstruction des données manquantes est une application typique de l’acquisition compressée. Une autre application d’intérêt correspond aux acquisitions Doppler duplex où des stratégies d’entrelacement des acquisitions sont nécessaires et conduisent donc à une réduction de la quantité de données disponibles. Dans ce contexte, nous avons réalisé de nouveaux développements permettant l’application de l’acquisition compressée à ces deux modalités d’acquisition ultrasonore. Dans un premier temps, nous avons proposé d’utiliser des dictionnaires redondants construits à partir des signaux d’intérêt pour la reconstruction d’images 3D ultrasonores. Une attention particulière a aussi été apportée à la configuration du système d’acquisition et nous avons choisi de nous concentrer sur un échantillonnage des lignes RF entières, réalisable en pratique de façon relativement simple. Cette méthode est validée sur données 3D simulées et expérimentales. Dans un deuxième temps, nous proposons une méthode qui permet d’alterner de manière aléatoire les émissions Doppler et les émissions destinées à l’imagerie mode-B. La technique est basée sur une approche bayésienne qui exploite la corrélation et la parcimonie des blocs du signal. L’algorithme est validé sur des données Doppler simulées et expérimentalesThis thesis is dedicated to the application of the novel compressed sensing theory to the acquisition and reconstruction of 3D US images and Doppler signals. In 3D US imaging, one of the major difficulties concerns the number of RF lines that has to be acquired to cover the complete volume. The acquisition of each line takes an incompressible time due to the finite velocity of the ultrasound wave. One possible solution for increasing the frame rate consists in reducing the acquisition time by skipping some RF lines. The reconstruction of the missing information in post processing is then a typical application of compressed sensing. Another excellent candidate for this theory is the Doppler duplex imaging that implies alternating two modes of emission, one for B-mode imaging and the other for flow estimation. Regarding 3D imaging, we propose a compressed sensing framework using learned overcomplete dictionaries. Such dictionaries allow for much sparser representations of the signals since they are optimized for a particular class of images such as US images.We also focus on the measurement sensing setup and propose a line-wise sampling of entire RF lines which allows to decrease the amount of data and is feasible in a relatively simple setting of the 3D US equipment. The algorithm was validated on 3D simulated and experimental data. For the Doppler application, we proposed a CS based framework for randomly interleaving Doppler and US emissions. The proposed method reconstructs the Doppler signal using a block sparse Bayesian learning algorithm that exploits the correlation structure within a signal and has the ability of recovering partially sparse signals as long as they are correlated. This method is validated on simulated and experimental Doppler data
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Iborra, Carreres Amadeo. "Development of a New 3D Reconstruction Algorithm for Computed Tomography (CT)." Doctoral thesis, Universitat Politècnica de València, 2016. http://hdl.handle.net/10251/59421.
Full textAbstract:
[EN] Model-based computed tomography (CT) image reconstruction is dominated by iterative algorithms. Although long reconstruction times remain as a barrier in practical applications, techniques to speed up its convergence are object of investigation, obtaining impressive results. In this thesis, a direct algorithm is proposed for model-based image reconstruction. The model-based approximation relies on the construction of a model matrix that poses a linear system which solution is the reconstructed image. The proposed algorithm consists in the QR decomposition of this matrix and the resolution of the system by a backward substitution process. The cost of this image reconstruction technique is a matrix vector multiplication and a backward substitution process, since the model construction and the QR decomposition are performed only once, because of each image reconstruction corresponds to the resolution of the same CT system for a different right hand side.
Several problems regarding the implementation of this algorithm arise, such as the exact calculation of a volume intersection, definition of fill-in reduction strategies optimized for CT model matrices, or CT symmetry exploit to reduce the size of the system. These problems have been detailed and solutions to overcome them have been proposed, and as a result, a proof of concept implementation has been obtained.
Reconstructed images have been analyzed and compared against the filtered backprojection (FBP) and maximum likelihood expectation maximization (MLEM) reconstruction algorithms, and results show several benefits of the proposed algorithm. Although high resolutions could not have been achieved yet, obtained results also demonstrate the prospective of this algorithm, as great performance and scalability improvements would be achieved with the success in the development of better fill-in strategies or additional symmetries in CT geometry.[ES] En la reconstrucción de imagen de tomografía axial computerizada (TAC), en su modalidad model-based, prevalecen los algoritmos iterativos. Aunque los altos tiempos de reconstrucción aún son una barrera para aplicaciones prácticas, diferentes técnicas para la aceleración de su convergencia están siendo objeto de investigación, obteniendo resultados impresionantes. En esta tesis, se propone un algoritmo directo para la reconstrucción de imagen model-based. La aproximación model-based se basa en la construcción de una matriz modelo que plantea un sistema lineal cuya solución es la imagen reconstruida. El algoritmo propuesto consiste en la descomposición QR de esta matriz y la resolución del sistema por un proceso de sustitución regresiva. El coste de esta técnica de reconstrucción de imagen es un producto matriz vector y una sustitución regresiva, ya que la construcción del modelo y la descomposición QR se realizan una sola vez, debido a que cada reconstrucción de imagen supone la resolución del mismo sistema TAC para un término independiente diferente. Durante la implementación de este algoritmo aparecen varios problemas, tales como el cálculo exacto del volumen de intersección, la definición de estrategias de reducción del relleno optimizadas para matrices de modelo de TAC, o el aprovechamiento de simetrías del TAC que reduzcan el tama\~no del sistema. Estos problemas han sido detallados y se han propuesto soluciones para superarlos, y como resultado, se ha obtenido una implementación de prueba de concepto. Las imágenes reconstruidas han sido analizadas y comparadas frente a los algoritmos de reconstrucción filtered backprojection (FBP) y maximum likelihood expectation maximization (MLEM), y los resultados muestran varias ventajas del algoritmo propuesto. Aunque no se han podido obtener resoluciones altas aún, los resultados obtenidos también demuestran el futuro de este algoritmo, ya que se podrían obtener mejoras importantes en el rendimiento y la escalabilidad con el éxito en el desarrollo de mejores estrategias de reducción de relleno o simetrías en la geometría TAC.
[CAT] En la reconstrucció de imatge tomografia axial computerizada (TAC) en la seua modalitat model-based prevaleixen els algorismes iteratius. Tot i que els alts temps de reconstrucció encara són un obstacle per a aplicacions pràctiques, diferents tècniques per a l'acceleració de la seua convergència estàn siguent objecte de investigació, obtenint resultats impressionants. En aquesta tesi, es proposa un algorisme direct per a la recconstrucció de image model-based. L'aproximació model-based es basa en la construcció d'una matriu model que planteja un sistema lineal quina sol·lució es la imatge reconstruida. L'algorisme propost consisteix en la descomposició QR d'aquesta matriu i la resolució del sistema per un procés de substitució regresiva. El cost d'aquesta tècnica de reconstrucció de imatge es un producte matriu vector i una substitució regresiva, ja que la construcció del model i la descomposició QR es realitzen una sola vegada, degut a que cada reconstrucció de imatge suposa la resolució del mateix sistema TAC per a un tèrme independent diferent. Durant la implementació d'aquest algorisme sorgixen diferents problemes, tals com el càlcul exacte del volum de intersecció, la definició d'estratègies de reducció de farcit optimitzades per a matrius de model de TAC, o el aprofitament de simetries del TAC que redueixquen el tamany del sistema. Aquestos problemes han sigut detallats y s'han proposat solucions per a superar-los, i com a resultat, s'ha obtingut una implementació de prova de concepte. Les imatges reconstruides han sigut analitzades i comparades front als algorismes de reconstrucció filtered backprojection (FBP) i maximum likelihood expectation maximization (MLEM), i els resultats mostren varies ventajes del algorisme propost. Encara que no s'han pogut obtindre resolucions altes ara per ara, els resultats obtinguts també demostren el futur d'aquest algorisme, ja que es prodrien obtindre millores importants en el rendiment i la escalabilitat amb l'éxit en el desemvolupament de millors estratègies de reducció de farcit o simetries en la geometria TAC.
Iborra Carreres, A. (2015). Development of a New 3D Reconstruction Algorithm for Computed Tomography (CT) [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/59421
TESIS
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Agier, Rémi. "Recalage de groupes d’images médicales 3D par extraction de points d’intérêt." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEI093/document.
Full textAbstract:
Les imageurs des hôpitaux produisent de plus en plus d'images 3D et il y a un nombre croissant d'études de cohortes. Afin d'ouvrir la voie à des méthodes utilisant de larges bases de données, il est nécessaire de développer des approches permettant de rendre ces bases cohérentes en recalant les images. Les principales méthodes actuelles de recalage de groupes utilisent des données denses (voxels) et sélectionnent une référence pour mettre en correspondance l'ensemble des images. Nous proposons une approche de recalage par groupes, sans image de référence, en utilisant seulement des points d'intérêt (Surf3D), applicable à des bases de plusieurs centaines d'images médicales. Nous formulons un problème global fondé sur l'appariement de points d'intérêt. La variabilité inter-individu étant grande, le taux de faux positifs (paires aberrantes) peut être très important (70\%). Une attention particulière est portée sur l'élimination des appariements erronés. Une première contribution effectue le recalage rigide de groupes d'images. Nous calculons les recalages de toutes les paires d'images. En s'appuyant sur le graphe complet de ces recalages, nous formulons le problème global en utilisant l'opérateur laplacien. Des expérimentations avec 400 images scanner CT 3D hétérogènes illustrent la robustesse de notre méthode et sa vitesse d'exécution. Une seconde contribution calcule le recalage déformable de groupes d'images. Nous utilisons des demi-transformations, paramétrées par des pyramides de B-splines, entre chaque image et un espace commun. Des comparaisons sur un jeu de données de référence montrent que notre approche est compétitive avec la littérature tout en étant beaucoup plus rapide. Ces résultats montrent le potentiel des approches basées sur des points d'intérêt pour la mise en correspondance de grandes bases d'images. Nous illustrons les perspectives de notre approche par deux applications : la segmentation multi-atlas et l'anthropologieThe ever-increasing amount of medical images stored in hospitals offers a great opportunity for big data analysis. In order to pave the way for huge image groups screening, we need to develop methods able to make images databases consistent by group registering those images. Currently, group registration methods generally use dense, voxel-based, representations for images and often pick a reference to register images. We propose a group registration framework, without reference image, by using only interest points (Surf3D), able to register hundreds of medical images. We formulate a global problem based on interest point matching. The inter-patient variability is high, and the outliers ratio can be large (70\%). We pay a particular attention on inhibiting outliers contribution. Our first contribution is a two-step rigid groupwise registration. In the first step, we compute the pairwise rigid registration of each image pair. In a second step, a complete graph of those registrations allows us to formulate a global problem using the laplacian operator. We show experimental results for groups of up to 400 CT-scanner 3D heterogeneous images highlighting the robustness and speed of our approach. In our second contribution, we compute a non-rigid groupwise registration. Our approach involves half-transforms, parametrized by a b-spline pyramid, between each image and a common space. A reference dataset shows that our algorithm provides competitive results while being much faster than previous methods. Those results show the potential of our interest point based registration method for huge datasets of 3D medical images. We also provide to promising perspectives: multi-atlas based segmentation and anthropology
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Li, Fan. "Segmentation and Symbolic Representation of Brain Vascular Network : Application to ArterioVenous Malformations." Thesis, Paris Est, 2016. http://www.theses.fr/2016PESC1048/document.
Full textAbstract:
Le traitement et l’analyse d’images angiographiques rotationnelles 3D (3DRA) de haute résolution spatiale pour l’aide à la planification d’interventions en neuroradiologie interventionnelle est un domaine de recherche récent et en plein essor. Les neuroradiologues ont besoin d’outils interactifs pour la planification des procédures d’embolisation et l’optimisation du guidage de microcathéters durant les interventions endovasculaires. L’exploitation des données d’imagerie pour l’aide au diagnostic et la thérapeutique requiert le développement d’algorithmes robustes et de méthodes efficaces. Ces méthodes permettent d’intégrer les informations contenues dans ces images pour en extraire des descripteurs anatomiques utiles durant les phases pre et per-opératoires.Cette thèse est dédiée au développement d’une chaine de traitement complète comprenant la segmentation, la reconstruction tridimensionnelle (3D) et la représentation symbolique de vaisseaux cérébraux à partir d’images 3DRA, pour faciliter la planification d’interventions d’embolisation pour le traitement de Malformations ArtérioVeineuses cérébrales (MAVs).La première partie du travail est consacrée à l’étude des différentes approches utilisées en segmentation des vaisseaux. Deux méthodes de segmentation sont ensuite proposées. Tout d’abord, une méthode de segmentation 2D coupe par coupe est développée ainsi qu’un technique robuste de suivi de vaisseaux permettant de détecter les bifurcations et de poursuivre le tracking de plusieurs branches du même vaisseau. Un maillage basé sur la triangulation Contrainte de Delaunay permet ensuite la reconstruction et la visualisation 3D des vaisseaux ainsi obtenus. Une méthode de segmentation 3D automatisée des images 3DRA est ensuite développée, elle présente l’avantage d’être plus rapide et de traiter le volume d’images entier en 3D. Cette méthode est basée sur la croissance de régions. Le processus 3D démarre à partir d’une coupe initiale pré-segmentée en utilisant la reconstruction géodésique et sur laquelle les germes sont placés de manière automatique. Finalement, une représentation du réseau vasculaire sur laquelle on distingue clairement les trois entités que sont les artères, les veines drainantes et le nidus est obtenue.La deuxième partie de la thèse est consacrée à la représentation symbolique des vaisseaux. L'étude hiérarchique du squelette permet de donner une description graphique du réseau vasculaire cérébral. A partir de cette description graphique, les vaisseaux et leurs branches sont labellisés et un ou plusieurs vaisseaux peuvent être isolés du reste du réseau pour une analyse visuelle plus précise, ce qui n’est pas possible avec les reconstructions 3D du constructeur. De plus, cette représentation améliore la détermination des chemins optimaux pour l’embolisation de la MAV et réduit la complexité due à l’enchevêtrement des vaisseaux malformés.La chaine de traitement complète ainsi développée aboutit à une description 3D précise des vaisseaux. Elle permet une meilleure compréhension structurelle du réseau vasculaire cérébral et offre aux neuroradiologues la possibilité d’extraire des descripteurs anatomiques, et géométriques (taille, diamètre…) des vaisseaux. Enfin, une étape de vérification des résultats par un expert neuroradiologue a permis la validation clinique des résultats de segmentation et de reconstruction 3D. L’intégration des algorithmes développés dans une interface graphique intuitive et facile d’utilisation devra être faite pour permettre l’exploitation de nos résultats en routine cliniqueThe processing and analysis of 3D Rotational Angiographic images (3DRA) of high spatial resolution to facilitate intervention planning in interventional neuroradiology is a new and booming research area. Neuroradiologists need interactive tools for the planning of embolization procedures and the optimization of the guidance of micro-catheters during endovascular interventions. The exploitation of imaging data to help in diagnosis and treatment requires the development of robust algorithms and efficient methods. These methods allow integrating information included in these images in order to extract useful anatomical descriptors during preoperative and peroperative phases.This thesis is dedicated to the development of a complete processing pipeline including segmentation, three-dimensional (3D) reconstruction and symbolic representation of cerebral vessels from 3DRA images, aiming to facilitate the embolization intervention planning for the treatment of cerebral ArterioVenous Malformations (AVMs).The first part of the work is devoted to the study of the different approaches used for the segmentation of vessels. Two segmentation methods are then proposed. First, a 2D slice-by-slice segmentation method is developed, followed by a robust vessel tracking process that enables detecting bifurcations and further following several branches of the same vessel. A mesh based on the Constrained Delaunay triangulation allows then the 3D reconstruction and visualization of the obtained vessels. An automated 3D segmentation method of 3DRA images is then developed, which presents the advantage of being faster and processing the whole 3D volume of images. This method is region growing based. The 3D process starts from an initial pre-segmented slice using the geodesic reconstruction, where the seeds are automatically placed. Finally, a representation of the vasculature is obtained, in which these three entities are clearly visible: the feeding arteries, the draining veins and the nidus.The second part of the thesis is devoted to the symbolic representation of the vessels. The hierarchical study of the skeleton allows giving a graphic description of the cerebral vascular network. From this graphic description, the vessels and their branches are labeled and one or more vessels can be isolated from the rest of network for a more accurate visual analysis, which is not possible with the original 3D reconstructions. Moreover, this improves the determination of the optimal paths for the AVM embolization and reduces the complexity due to the entanglement of the malformed vessels.The complete processing pipeline thus developed leads to a precise 3D description of the vessels. It allows a better understanding of the cerebral vascular network structure and provides the possibility to neuroradiologists of extracting anatomical and geometric descriptors (size, diameter...) of the vessels. Finally, a verification step of the results by a neuroradiology expert enabled clinical validation of the 3D segmentation and reconstruction results. The integration of the developed algorithms in a user-friendly graphical interface should be achieved to allow the exploitation of our results in clinical routine
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Decker, Summer J. "The Human in 3D: Advanced Morphometric Analysis of High-Resolution Anatomically Accurate Computed Models." Scholar Commons, 2010. http://scholarcommons.usf.edu/etd/3525.
Full textAbstract:
Computed virtual models of anatomical structures are proving to be of increasing value in clinical medicine, education and research. With a variety of fields focused on craniofacial and pelvic anatomy there is a need for accurate anatomical models. Recent technological advancements in computer and medical imaging technologies have provided the tools necessary to develop three-dimensional (3D) functional models of human anatomy for use in medicine (surgical planning and education), forensics and engineering (computer-aided design (CAD) and finite element analysis).
Traditionally caliper methodologies are used in the quantitative analysis of human anatomy. In order for experts in anatomy and morphometrics to accept a transition to 3D volumetric data, it must be first validated as anatomically accurate. The purpose of this project was to create anatomically accurate models of modern human anatomy through the use of 3D medical imaging, such as multislice computed tomography (CT), and 3D computer modeling and reconstruction. This dissertation attempts to validate the models and address current morphometric methodologies with four separate studies. The important results found in these studies were:
1) Medical image data such as computed tomography scans can be used to create high-resolution anatomically accurate 3D models for education and research purposes. These models can be used in morphometric studies through virtual quantitative analyses.
2) 3D virtual models of the human pelvis are 100% accurate in the estimation of sex in the pelvis, which represents an increase in accuracy over current field methods.
3) 3D virtual models of the human skull are 95.1% accurate in estimating sex in the skull, which represents an increase in accuracy over current field methods.
4) 3D models of craniofacial anatomy can be used for soft tissue depth analysis studies and clinical image data is more representative of living individuals.
By testing the imaging and 3D modeling technologies at several levels, we developed new methods for accurately analyzing virtual anatomy for an array of disciplines.