261 research outputs found

    A topological sampling theorem for Robust boundary reconstruction and image segmentation

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    AbstractExisting theories on shape digitization impose strong constraints on admissible shapes, and require error-free data. Consequently, these theories are not applicable to most real-world situations. In this paper, we propose a new approach that overcomes many of these limitations. It assumes that segmentation algorithms represent the detected boundary by a set of points whose deviation from the true contours is bounded. Given these error bounds, we reconstruct boundary connectivity by means of Delaunay triangulation and α-shapes. We prove that this procedure is guaranteed to result in topologically correct image segmentations under certain realistic conditions. Experiments on real and synthetic images demonstrate the good performance of the new method and confirm the predictions of our theory

    Tangent-ball techniques for shape processing

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    Shape processing defines a set of theoretical and algorithmic tools for creating, measuring and modifying digital representations of shapes.  Such tools are of paramount importance to many disciplines of computer graphics, including modeling, animation, visualization, and image processing.  Many applications of shape processing can be found in the entertainment and medical industries. In an attempt to improve upon many previous shape processing techniques, the present thesis explores the theoretical and algorithmic aspects of a difference measure, which involves fitting a ball (disk in 2D and sphere in 3D) so that it has at least one tangential contact with each shape and the ball interior is disjoint from both shapes. We propose a set of ball-based operators and discuss their properties, implementations, and applications.  We divide the group of ball-based operations into unary and binary as follows: Unary operators include: * Identifying details (sharp, salient features, constrictions) * Smoothing shapes by removing such details, replacing them by fillets and roundings * Segmentation (recognition, abstract modelization via centerline and radius variation) of tubular structures Binary operators include: * Measuring the local discrepancy between two shapes * Computing the average of two shapes * Computing point-to-point correspondence between two shapes * Computing circular trajectories between corresponding points that meet both shapes at right angles * Using these trajectories to support smooth morphing (inbetweening) * Using a curve morph to construct surfaces that interpolate between contours on consecutive slices The technical contributions of this thesis focus on the implementation of these tangent-ball operators and their usefulness in applications of shape processing. We show specific applications in the areas of animation and computer-aided medical diagnosis.  These algorithms are simple to implement, mathematically elegant, and fast to execute.Ph.D.Committee Chair: Jarek Rossignac; Committee Member: Greg Slabaugh; Committee Member: Greg Turk; Committee Member: Karen Liu; Committee Member: Maryann Simmon

    Courbure discrète : théorie et applications

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    International audienceThe present volume contains the proceedings of the 2013 Meeting on discrete curvature, held at CIRM, Luminy, France. The aim of this meeting was to bring together researchers from various backgrounds, ranging from mathematics to computer science, with a focus on both theory and applications. With 27 invited talks and 8 posters, the conference attracted 70 researchers from all over the world. The challenge of finding a common ground on the topic of discrete curvature was met with success, and these proceedings are a testimony of this wor

    Retinal Fundus Image Analysis for Diagnosis of Glaucoma: A Comprehensive Survey

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    © 2016 IEEE. The rapid development of digital imaging and computer vision has increased the potential of using the image processing technologies in ophthalmology. Image processing systems are used in standard clinical practices with the development of medical diagnostic systems. The retinal images provide vital information about the health of the sensory part of the visual system. Retinal diseases, such as glaucoma, diabetic retinopathy, age-related macular degeneration, Stargardt's disease, and retinopathy of prematurity, can lead to blindness manifest as artifacts in the retinal image. An automated system can be used for offering standardized large-scale screening at a lower cost, which may reduce human errors, provide services to remote areas, as well as free from observer bias and fatigue. Treatment for retinal diseases is available; the challenge lies in finding a cost-effective approach with high sensitivity and specificity that can be applied to large populations in a timely manner to identify those who are at risk at the early stages of the disease. The progress of the glaucoma disease is very often quiet in the early stages. The number of people affected has been increasing and patients are seldom aware of the disease, which can cause delay in the treatment. A review of how computer-aided approaches may be applied in the diagnosis and staging of glaucoma is discussed here. The current status of the computer technology is reviewed, covering localization and segmentation of the optic nerve head, pixel level glaucomatic changes, diagonosis using 3-D data sets, and artificial neural networks for detecting the progression of the glaucoma disease

    Liver segmentation in MRI: a fully automatic method based on stochastic partitions

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    There are few fully automated methods for liver segmentation in magnetic resonance images (MRI) despite the benefits of this type of acquisition in comparison to other radiology techniques such as computed tomography (CT). Motivated by medical requirements, liver segmentation in MRI has been carried out. For this purpose, we present a new method for liver segmentation based on the watershed transform and stochastic partitions. The classical watershed over-segmentation is reduced using a marker-controlled algorithm. To improve accuracy of selected contours, the gradient of the original image is successfully enhanced by applying a new variant of stochastic watershed. Moreover, a final classifier is performed in order to obtain the final liver mask. Optimal parameters of the method are tuned using a training dataset and then they are applied to the rest of studies (17 datasets). The obtained results (a Jaccard coefficient of 0.91 +/- 0.02) in comparison to other methods demonstrate that the new variant of stochastic watershed is a robust tool for automatic segmentation of the liver in MRI. (C) 2014 Elsevier Ireland Ltd. All rights reserved.This work has been supported by the MITYC under the project NaRALap (ref. TSI-020100-2009-189), partially by the CDTI under the project ONCOTIC (IDI-20101153), by Ministerio de Educacion y Ciencia Spain, Project Game Teen (TIN2010-20187) projects Consolider-C (SEJ2006-14301/PSIC), "CIBER of Physiopathology of Obesity and Nutrition, an initiative of ISCIII" and Excellence Research Program PROMETEO (Generalitat Valenciana. Conselleria de Educacion, 2008-157). We would like to express our gratitude to the Hospital Clinica Benidorm, for providing the MR datasets and to the radiologist team of Inscanner for the manual segmentation of the MR images.López-Mir, F.; Naranjo Ornedo, V.; Angulo, J.; Alcañiz Raya, ML.; Luna, L. (2014). Liver segmentation in MRI: a fully automatic method based on stochastic partitions. Computer Methods and Programs in Biomedicine. 114(1):11-28. https://doi.org/10.1016/j.cmpb.2013.12.022S1128114

    Multi-scale active shape description in medical imaging

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    Shape description in medical imaging has become an increasingly important research field in recent years. Fast and high-resolution image acquisition methods like Magnetic Resonance (MR) imaging produce very detailed cross-sectional images of the human body - shape description is then a post-processing operation which abstracts quantitative descriptions of anatomically relevant object shapes. This task is usually performed by clinicians and other experts by first segmenting the shapes of interest, and then making volumetric and other quantitative measurements. High demand on expert time and inter- and intra-observer variability impose a clinical need of automating this process. Furthermore, recent studies in clinical neurology on the correspondence between disease status and degree of shape deformations necessitate the use of more sophisticated, higher-level shape description techniques. In this work a new hierarchical tool for shape description has been developed, combining two recently developed and powerful techniques in image processing: differential invariants in scale-space, and active contour models. This tool enables quantitative and qualitative shape studies at multiple levels of image detail, exploring the extra image scale degree of freedom. Using scale-space continuity, the global object shape can be detected at a coarse level of image detail, and finer shape characteristics can be found at higher levels of detail or scales. New methods for active shape evolution and focusing have been developed for the extraction of shapes at a large set of scales using an active contour model whose energy function is regularized with respect to scale and geometric differential image invariants. The resulting set of shapes is formulated as a multiscale shape stack which is analysed and described for each scale level with a large set of shape descriptors to obtain and analyse shape changes across scales. This shape stack leads naturally to several questions in regard to variable sampling and appropriate levels of detail to investigate an image. The relationship between active contour sampling precision and scale-space is addressed. After a thorough review of modem shape description, multi-scale image processing and active contour model techniques, the novel framework for multi-scale active shape description is presented and tested on synthetic images and medical images. An interesting result is the recovery of the fractal dimension of a known fractal boundary using this framework. Medical applications addressed are grey-matter deformations occurring for patients with epilepsy, spinal cord atrophy for patients with Multiple Sclerosis, and cortical impairment for neonates. Extensions to non-linear scale-spaces, comparisons to binary curve and curvature evolution schemes as well as other hierarchical shape descriptors are discussed

    Dynamical models and machine learning for supervised segmentation

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    This thesis is concerned with the problem of how to outline regions of interest in medical images, when the boundaries are weak or ambiguous and the region shapes are irregular. The focus on machine learning and interactivity leads to a common theme of the need to balance conflicting requirements. First, any machine learning method must strike a balance between how much it can learn and how well it generalises. Second, interactive methods must balance minimal user demand with maximal user control. To address the problem of weak boundaries,methods of supervised texture classification are investigated that do not use explicit texture features. These methods enable prior knowledge about the image to benefit any segmentation framework. A chosen dynamic contour model, based on probabilistic boundary tracking, combines these image priors with efficient modes of interaction. We show the benefits of the texture classifiers over intensity and gradient-based image models, in both classification and boundary extraction. To address the problem of irregular region shape, we devise a new type of statistical shape model (SSM) that does not use explicit boundary features or assume high-level similarity between region shapes. First, the models are used for shape discrimination, to constrain any segmentation framework by way of regularisation. Second, the SSMs are used for shape generation, allowing probabilistic segmentation frameworks to draw shapes from a prior distribution. The generative models also include novel methods to constrain shape generation according to information from both the image and user interactions. The shape models are first evaluated in terms of discrimination capability, and shown to out-perform other shape descriptors. Experiments also show that the shape models can benefit a standard type of segmentation algorithm by providing shape regularisers. We finally show how to exploit the shape models in supervised segmentation frameworks, and evaluate their benefits in user trials

    In-house Implementation and Validation of the Mid-Position CT approach for the Treatment Planning of Respiration-induced Moving Tumours in Radiotherapy for Lung and Upper abdomen cancer

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    Tese mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica) Universidade de Lisboa, Faculdade de Ciências, 2022A Radioterapia é uma das modalidades principais para tratamentos de foro oncológico que visa destruir a ação proliferativa das células cancerígenas e reduzir o volume tumoral. A sua ação terapêutica através do uso de radiação ionizante tem, subjacente, a máxima de irradiar o tumor com uma elevada dose, ao mesmo tempo que os órgãos de risco (OARs) adjacentes, são tanto quanto possível protegidos. Quando um tumor se localiza no pulmão ou abdómen superior, como no fígado ou pâncreas, o seu movimento devido à respiração pode alcançar até 4 cm, especialmente na direção crânio-caudal, aumentando as incertezas relativas à posição do tumor. No Centro Clínico Champalimaud (CCC), o planeamento convencional dos tratamentos de radioterapia faz uso de uma tomografia computadorizada (CT) que é adquirida aquando da respiração livre do doente e que, por isso, apresenta geralmente artefactos que podem ser uma fonte de erro durante o planeamento. Nos casos em que o movimento do tumor é considerável, é ainda adquirida uma tomografia computadorizada quadrimensional (4DCT) que consiste entre 8 e 10 CTs que representam fases do ciclo respiratório. Posteriormente, a 4DCT é utilizada para delinear o volume interno do alvo (ITV) que engloba toda a extensão do movimento do tumor. Apesar da estratégia do ITV garantir uma adequada cobertura do volume-alvo, os OARs ficam expostos a doses de radiação desnecessárias e a um maior risco de toxicidade. Este efeito é ainda mais preocupante em tratamentos hipofracionados, onde doses mais elevadas são administradas num número reduzido de frações. Nos últimos anos têm sido desenvolvidas estratégias que visam tornar os tratamentos de radioterapia mais eficazes. Uma delas é a reconstrução de uma CT que representa a posição média do doente ao longo do ciclo respiratório (Mid-P CT). Esta estratégia resulta em volumes de tratamento menores do que a estratégia do ITV, possibilitando o aumento da dose e maior controlo tumoral local. O primeiro passo para a reconstrução do Mid-P CT é o registo deformável de imagens (DIR) entre uma das fases da respiração (uma CT da 4DCT), definida como a fase de referência, e as restantes fases. Deste processo resultam campos vetoriais deformáveis (DVF) que contém informação do deslocamento dos tecidos. Os DVFs são subsequentemente utilizados para transformar cada uma das fases da respiração para a posição média. O método do Mid-P foi implementado com sucesso no Instituto do Cancro Holandês (NKI) em 2008. Apesar dos bons resultados clínicos, o número de centros de radioterapia que utiliza esta técnica é muito reduzido. Tal deve-se, por um lado, à inexistência de soluções comerciais com esta funcionalidade e, por outro, ao esforço necessário alocar para implementar e validar soluções desenvolvidas internamente. O presente projeto teve como principal objetivo implementar a estratégia do Mid-P no CCC (Portugal). Para tal, foi otimizado um módulo – RunMidP – desenvolvido para o software 3D Slicer, que calcula o Mid-P CT e estima a amplitude do movimento do tumor e OARs com base nos DVFs. Considerando que a precisão do módulo e a qualidade de imagem do Mid-P CT devem atender os requisitos para o planeamento em radioterapia, foram realizados testes para validar o módulo. Sempre que possível, a sua performance foi comparada com outras aplicações desenvolvidas para a implementação da técnica do Mid-P, nomeadamente com um protótipo desenvolvido pela empresa Mirada Medical Ltd. (Reino Unido) – Mirada – e com o software desenvolvido no NKI (Holanda) – Wimp. Os testes foram divididos em três estudos diferentes, cada um com um conjunto de dados diferente. No primeiro estudo (estudo A), foram utilizadas 4DCT de 2 fantomas digitais, cuja função respiratória e cardíaca foi modelada de forma simplificada, e de 18 doentes com tumores localizados no pulmão (N = 8), no fígado (N = 6) e no pâncreas (N = 4). Neste estudo, foram comparados dois algoritmos DIR disponíveis no software 3D Slicer, o Plastimatch e o Elastix, em termos da precisão do registo e da qualidade de imagem do Mid-P CT reconstruído. Foi ainda avaliado a capacidade dos softwares RunMidP e Mirada representarem corretamente a posição média do doente e as diferenças das amplitudes do movimento do tumor estimadas pelos dois softwares. No estudo B, foram realizados testes de verificação semelhantes aos supre mencionados, em imagens sintéticas provenientes de 16 doentes, desta vez com a vantagem de se conhecer o “verdadeiro” Mid-P CT e as “verdadeiras” amplitudes do movimento do tumor. Estes foram comparados com os resultados obtidos com os softwares RunMidP e Mirada. Ainda, as unidades de Hounsfield (HU) no Mid-P CT reconstruído por RunMidP e Mirada foram comparadas com as HU na fase de referência, de modo a verificar se os Mid P CTs produziriam diferenças dosimétricas relevantes. No último estudo (estudo C), a qualidade de imagem do Mid-P CT foi avaliada quantitativamente e qualitativamente. Durante a análise qualitativa, foi pedido a dois médicos especialistas que avaliassem a viabilidade dos Mid-P CTs, reconstruídos pelos três softwares (RunMidP, Mirada e Wimp), para o planeamento dos tratamentos. O tempo da reconstrução do Mid-P CT a partir da 4DCT foi de cerca de 1h. Ambos os algoritmos, Plastimach e Elastix, demonstraram ser adequados para DIR de imagens do pulmão e abdómen superior, com diferenças estatisticamente não significativas (p > 0.05) em termos da precisão do registo. Contudo, o Mid-P CT reconstruído com Elastix apresentou uma melhoria na qualidade de imagem, sendo assim o algoritmo DIR escolhido para ser implementado no RunMidP. Em termos de métricas aplicadas a contornos definidos manualmente, tais como a distância de Hausdorf (HD) e coeficiente de Dice (DSC), o erro do registo de imagem foi menor que 1 mm, dentro do contorno do tumor, e 2 mm no pulmão. Os Mid-P CTs reconstruídos com o RunMidP e Mirada apresentaram maiores diferenças, relativamente ao “verdadeiro” Mid-P CT, na região do diafragma e zonas de maior homogeneidade como, por exemplo, no ar presente no intestino. Contudo, para a maioria dos doentes do estudo B, o Mid-P CT reconstruído com o software Mirada apresentou maior índice de similaridade estrutural (SSIM) relativamente ao “verdadeiro” Mid-P CT. Estes resultados podem estar na origem do uso de diferentes algoritmos DIR, mas deveram-se principalmente a uma falha na aplicação das transformações deformáveis pelo módulo RunMiP que foi corrigida posteriormente. Ainda, as diferenças entre as amplitudes estimadas e previstas foram menores que 1 mm para 37 tumores (78,9%), que resultam em diferenças menores que 0.3mm quando convertidas em margens de planeamento. Para além disso, as diferenças nos valores de HU dos Mid-P CTs comparativamente à fase de referência foram, em média, de 1 HU no tumor e OARs. Foram também observadas melhorias na qualidade de imagem do Mid-P CT, nomeadamente um aumento da relação sinal-ruído (SNR) e diminuição dos artefactos. Estes resultados estão de acordo com a avaliação dos médicos que, em geral, consideraram que os Mid-P CTs reconstruídos pelos três softwares são adequados para o planeamento dos tratamentos. No entanto, os Mid-P CTs reconstruídos com dados 4DCT provenientes do CCC apresentaram classificações inferiores aos reconstruídos com dados 4DCT do NKI. Em suma, as modificações do algoritmo DIR Plastimach para Elastix e a correção do método para aplicar as transformações deformáveis, permitiram uma melhoria na qualidade de imagem do Mid P CT e melhor performance do algoritmo, respetivamente. O módulo RunMidP, neste projeto otimizado e validado, apresenta um forte potencial para a reconstrução e implementação da estratégia do Mid-P na clínica, com performance comparável a outras aplicações existentes (Mirada e Wimp). Atenção especial deve ser dada aos dados 4DCT de input que parecem afetar a qualidade de imagem final do Mid-P CT. No futuro, valerá a pena otimizar os parâmetros de aquisição e reconstrução da 4DCT de modo a melhorar a qualidade de imagem e, ainda, o módulo RunMidP pode potencialmente ser otimizado no que respeita ao tempo de reconstrução do Mid-P CT e à precisão do DIR.Radiotherapy for tumours in the thorax and upper abdomen is challenging since they move notably with breathing. To cover the whole extent of tumour motion, relatively large margins are added to treatment volumes, posing a higher risk of toxicity for surrounding organs-at-risk (OARs). The Mid Position (Mid-P) method accounts for breathing motion by using deformable image registration (DIR) to transform all phases of a 4DCT scan to a time-weighted average 3DCT scan (Mid-P CT). The Mid-P strategy results in smaller treatment volumes, potentially boosting the delivery of hypofractionated treatments. To bring the Mid-P approach to the Champalimaud Clinical Centre (CCC), an in-house Mid position software module – RunMidP – was optimized. The module reconstructs the Mid-P CT and estimates breathing motion amplitudes of tumours and relevant OARs. In addition, this project presents a set of experiments to evaluate the performance of the Mid-P method and its feasibility for clinical implementation. The experiments were conducted throughout three different studies using 4DCT data from 18 phantoms and 23 patients. In Study A, the accuracy and image quality of two DIR algorithms (Plastimatch and Elastix) were assessed using quantitative metrics applied on either warped images or manually delineated contours. The reproduction of the patient’s mean position by the Mid-P CT and the estimation of motion amplitudes were compared to a soon-to-be Mid-P commercial software developed by Mirada Medical Ltd. In Study B,similar experiments were performed, this time using a more rigorous reference – “true” Mid-P CT scans and “true” motion estimations. In Study C, the image quality of Mid P CT scans was assessed quantitatively and qualitatively. Both Plastimatch and Elastix registration showed comparable registration accuracy, although Elastix showed superior image quality of reconstructed Mid-P CTs. Based on contour metrics, the registration error was less than 2 mm. In-house Mid-P CTs showed a slightly lower match to ground truth Mid-P CTs than the ones reconstructed by the Mirada prototype due to differences in DIR methods and small shifts to the original image geometry. Higher image differences were found in the diaphragm lung interface, where the patient's anatomy moves faster due to breathing, and in homogeneous regions such as the air regions in the bowel. On the other hand, differences (estimated-predicted) in motion amplitudes smaller than 1 mm were observed in 37 moving tumours (78.7%), showing a good performance of the Mid-P algorithm. Regarding the image quality, improvements in the signal-to-noise ratio and removal of image artefacts in Mid-P CTs are great advantages for using them as the planning CT. Clinicians also gave a good assessment of the suitability of Mid-P CT scans for treatment planning. No significant differences were found in the performance of the RunMidP compared to other Mid-Position packages, although worse scores were given to the CCC dataset than the dataset from another hospital. The in-house Mid-position algorithm shows promising results regarding the use of the software module in radiotherapy for lung and upper abdomen cancer. Further exploration must be given to improve the registration accuracy, image quality of the input data, and speed up the reconstruction of the Mid-P CT scan
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