Self-assessed Contrast-Maximizing Adaptive Region Growing

In the context of an experimental virtual-reality surgical planning software platform, we propose a fully self-assessed adaptive region growing segmentation algorithm. Our method successfully delineates main tissues relevant to head and neck reconstructive surgery, such as skin, fat, muscle/organs, and bone. We rely on a standardized and self-assessed region-based approach to deal with a great variety of imaging conditions with minimal user intervention, as only a single-seed selection stage is required. The detection of the optimal parameters is managed internally using a measure of the varying contrast of the growing regions. Validation based on synthetic images, as well as truly-delineated real CT volumes, is provided for the reader’s evaluation

A New Approach for Model-Based Adaptive Region Growing in Medical Image Analysis

Interaction increases flexibility of segmentation but it leads to undesirable behaviour of an algorithm if knowledge being requested is inappropriate

Inter-comparison of medical image segmentation algorithms

Segmentation of images is a vital part of medical image processing, and MRI (Magnetic Resonance Imaging) is already recognized as a very important tool for clinical diagnosis. In this thesis, comparisons between different segmentation algorithms are carried out, specifically on brain MRI images. Initial parts of the thesis provide the background to the project, and an introduction to the basic principles of MRI, respectively, followed by parameter definitions and MRI image artifacts. The next part briefly covers various image pre-processing techniques which are required, and this is followed with a review of the major segmentation techniques which are available, including thresholding, region growing, clustering, and K-Means clustering. The concept of fuzzy logic is also introduced here, and the chapter concludes with a discussion of fuzzy logic based segmentation algorithms such as Fuzzy C-Means (FCM) and Improved Fuzzy C-Means (IFCM) clustering algorithms. The following part provides details concerning the source, type and parameters of the data (images) used for this thesis. Evaluation and inter-comparisons between a number of different segmentation algorithms are given in near concluding part, finally, conclusions and suggestions for future research are provided in last part. Qualitative comparisons on real images and quantitative comparisons on simulated images were performed. Both qualitative and quantitative comparisons demonstrated that fuzzy logic based segmentation algorithms are superior in comparison with classical segmentation algorithms. Edge-based segmentation algorithms demonstrated the poorest performance of all; K-means and IFCM clustering algorithms performed better, and FCM demonstrated the best performance of all. However, it should be noted that IFCM was not properly evaluated due to time restrictions in code generation, testing and evaluation.Segmentation of images is a vital part of medical image processing, and MRI (Magnetic Resonance Imaging) is already recognized as a very important tool for clinical diagnosis. In this thesis, comparisons between different segmentation algorithms are carried out, specifically on brain MRI images. Initial parts of the thesis provide the background to the project, and an introduction to the basic principles of MRI, respectively, followed by parameter definitions and MRI image artifacts. The next part briefly covers various image pre-processing techniques which are required, and this is followed with a review of the major segmentation techniques which are available, including thresholding, region growing, clustering, and K-Means clustering. The concept of fuzzy logic is also introduced here, and the chapter concludes with a discussion of fuzzy logic based segmentation algorithms such as Fuzzy C-Means (FCM) and Improved Fuzzy C-Means (IFCM) clustering algorithms. The following part provides details concerning the source, type and parameters of the data (images) used for this thesis. Evaluation and inter-comparisons between a number of different segmentation algorithms are given in near concluding part, finally, conclusions and suggestions for future research are provided in last part. Qualitative comparisons on real images and quantitative comparisons on simulated images were performed. Both qualitative and quantitative comparisons demonstrated that fuzzy logic based segmentation algorithms are superior in comparison with classical segmentation algorithms. Edge-based segmentation algorithms demonstrated the poorest performance of all; K-means and IFCM clustering algorithms performed better, and FCM demonstrated the best performance of all. However, it should be noted that IFCM was not properly evaluated due to time restrictions in code generation, testing and evaluation

Segmentation And Spatial Depth Ridge Detection Of Unorganized Point Cloud Data

Visual 3D data are of interest to a number of fields: medical professionals, game designers, graphic designers, and (in the interest of this paper) ichthyologists interested in the taxonomy of fish. Since the release of the Kinect for the Microsoft XBox, game designers have been interested in using the 3D data returned by the device to understand human movement and translate that movement into an interface with which to interact with game systems. In the medical field, researchers must use computer vision tools to navigate through the data found in CT scans and MRI scans. These tools must segment images into the parts that are relevant to researchers and account for noise related to the scanning process all while ignoring other types of noise such as foreign elements in the body that might indicate signs of illness. 3D point cloud data represents some unique challenges. Consider an object scanned with a laser scanner. The scanner returns the surface points of the object, but nothing more. Using the tool Qhull, a researcher can quickly compute the convex hull of an object (which is an interesting challenge in itself), but the convex hull (obviously) leaves out any description of an object\u27s concave features. Several algorithms have been proposed to illustrate an object\u27s complete features based on unorganized 3D point cloud data as accurately as possible, most notably Boissonnat\u27s tetrahedral culling algorithm and The Power Crust algorithm. We introduce a new approach to the area partitioning problem that takes into consideration these algorithms\u27 strengths and weaknesses. In this paper we propose a methodology for approximating a shape\u27s solid geometry using the unorganized 3D point cloud data of that shape primarily by utilizing localized principal component analysis information. Our model accounts for three comissues that arise in the scanning of 3D objects: noise in surface points, poorly sampled surface area, and narrow corners. We explore each of these areas of concern and outline our approach to each. Our technique uses a growing algorithm that labels points as it progresses and uses those labels with a simple priority queue. We found that our approach works especially well for approximating surfaces under the condition where a local surface is poorly sampled (i.e a significant hole is present in the point cloud). We then turn to study the medial axis of a shape for the purposes of `unfolding\u27 that structure. Our approach uses a ridge formulation based on the spatial depth statistic to create the medial axis. We conclude the paper with visual results of our technique

Segmentation des structures céphaliques à topologie complexe

L’utilisation de l’imagerie médicale connaît un essor croissant dans la pratique de la médecine actuelle. Les acquisitions, qu’elles soient en deux ou trois dimensions, sont utilisées pour poser des gestes pré, per ou post-opératoires. Toutefois, la visualisation n’est pas toujours suffisante, il est souvent nécessaire de mesurer les volumétries précises des organes. Pour ce faire, il est impératif de délimiter avec précision les structures anatomiques d’intérêt à l’aide de méthodes de segmentation. Ce domaine de recherche connexe à l’imagerie médicale est en constante évolution et compte un grand nombre d’approches différentes. Certains systèmes utilisent des bases de données statistiques, d’autres n’utilisent que les données propres à l’image à traiter comme les Level-Sets. En fonction du type d’acquisition et/ou du type d’organe ciblé, certaines approches sont à préférer à d’autres. Toutefois, lorsque les structures d’intérêt présentent des surfaces à topologie complexe ou peuvent connaître une forte variation interindividuelle, les approches proposées dans la littérature peuvent être inefficaces. Les causes potentielles d’inefficacité peuvent être imputées, soit à un trop grand nombre de degrés de liberté (pas de connaissances a priori) ou à un trop grand nombre de contraintes (une connaissance a priori trop stricte). Dans les deux cas, le résultat de la segmentation risque d’être inexact et par conséquent inexploitable en clinique. Pour pallier ces limitations, la méthode proposée dans cette thèse utilise les données pour forger in-situ un modèle a priori fiable permettant une segmentation précise des organes ciblés. Le modèle généré permet de contraindre une segmentation par recherche de surface minimale 3D dans un espace plan restreint. La méthode développée est automatique, multimodalité et applicable à plusieurs types d’organes