Abstract-Modern medical imaging modalities provide large amounts of information in both the spatial and temporal domains and the incorporation of this information in a coherent algorithmic framework is a significant challenge. In this paper, we present a novel and intuitive approach to combine 3-D spatial and temporal (3-D + time) magnetic resonance imaging (MRI) data in an integrated segmentation algorithm to extract the myocardium of the left ventricle. A novel level-set segmentation process is developed that simultaneously delineates and tracks the boundaries of the left ventricle muscle. By encoding prior knowledge about cardiac temporal evolution in a parametric framework, an expectation-maximization algorithm optimally tracks the myocardial deformation over the cardiac cycle. The expectation step deforms the level-set function while the maximization step updates the prior temporal model parameters to perform the segmentation in a nonrigid sense.Index Terms-Cardiac magnetic resonance imaging (MRI), four-dimensional (4-D), level-set, segmentation, temporal model.

Abstract-Hyperspectral data allows the construction of more elaborate models to sample the properties of the nonferrous materials than the standard RGB color representation. In this paper, the nonferrous waste materials are studied as they cannot be sorted by classical procedures due to their color, weight and shape similarities. The experimental results presented in this paper reveal that factors such as the various levels of oxidization of the waste materials and the slight differences in their chemical composition preclude the use of the spectral features in a simplistic manner for robust material classification. To address these problems, the proposed FUSSER (FUzzy Spectral and Spatial classifiER) algorithm detailed in this paper merges the spectral and spatial features to obtain a combined feature vector that is able to better sample the properties of the nonferrous materials than the single pixel spectral features when applied to the construction of multivariate Gaussian distributions. This approach allows the implementation of statistical region merging techniques in order to increase the performance of the classification process. To achieve an efficient implementation, the dimensionality of the hyperspectral data is reduced by constructing bio-inspired spectral fuzzy sets that minimize the amount of redundant information contained in adjacent hyperspectral bands. The experimental results indicate that the proposed algorithm increased the overall classification rate from 44% using RGB data up to 98% when the spectral-spatial features are used for nonferrous material classification.

A novel approach for the automatic segmentation has been developed to extract the contours of the epi-cardium and endo-cardium boundary of the left ventricle of the heart. The developed segmentation scheme takes multi-slice and multi-phase Magnetic Resonance (MR) images of the heart, transversing the short-axis length from the base to the apex. Each image is taken at one instance in the heart's phase.The images are segmented using a diffusion-based filter followed by an unsupervised clustering technique and the resulting labels are checked to locate the left ventricle (lv ) cavity. From cardiac anatomy, the closest pool of blood to the lv cavity is the right ventricle cavity. The wall between these two blood-pools (interventricular septum) is measured to give an approximate thickness for the myocardium. This value is used when a radial search is performed on a gradient image to find appropriate robust segments of the epi-cardium boundary. The robust edge segments are then joined using a normal spline curve. Experimental results are presented with very encouraging qualitative and quantitative results and a comparison is made against the state-of-the art level-sets method.

This paper presents a coupled level-set segmentation of the myocardium of the left ventricle of the heart using a priori information. From a fast marching initialisation, two fronts representing the endocardium and epicardium boundaries of the left ventricle are evolved as the zero level-set of a higher dimension function. We introduce a novel and robust stopping term using both gradient and region-based information. The segmentation is supervised both with a coupling function and using a probabilistic model built from training instances. The robustness of the segmentation scheme is evaluated by performing a segmentation on four unseen data-sets containing high variation and the performance of the segmentation is quantitatively assessed.

Objective The purpose of this article is to determine the feasibility of using computer assisted diagnosis (CAD) techniques to automatically identify, localize, and measure body fat tissue from a rapid wholebody MRI examination Design Prospective investigative study.Setting Dublin, Ireland.Participants 42 volunteers (21 male, 21 female) aged 18 to 56. The study group consisted of healthy volunteers with a wide range of body weights, and included a cohort of high performance athletes. Main outcome measuresThe provision of an automated system, which assesses subjects' whole body MRI scans and which provides numerical and visual feedback to illustrate the findings. The system generates results in a matter of minutes allowing for an initial assessment to be performed immediately following the completion of an MRI scan. By highlighting areas where body fat is concentrated the system allows radiologists to quickly identify and examine regions of interest in the scan.Results Rapid, accurate, and reproducible delineation of body fat distribution can be obtained using whole body MRI techniques and Computer Aided Diagnosis.Conclusions Whole-body MRI in conjunction with CAD allows a fast, automatic, and accurate approach to body fat measurement and localization and can be a useful alternative to body mass index.Whole-body fat analysis can be achieved in less than 5 min.3

Abstract-The aim of this paper is to detail the development of a novel tracking framework that is able to extract the cell motility indicators and to determine the cellular division (mitosis) events in large time-lapse phase-contrast image sequences. To address the challenges induced by non-structured (random) motion, cellular agglomeration, and cellular mitosis, the process of automatic (unsupervised) cell tracking is carried out in a sequential manner, where the inter-frame cell association is achieved by assessing the variation in the local cellular structures in consecutive frames of the image sequence. In our study a strong emphasis has been placed on the robust use of the topological information in the cellular tracking process and in the development of targeted pattern recognition techniques that were designed to redress the problems caused by segmentation errors, and to precisely identify mitosis using a backward (reversed) tracking strategy. The proposed algorithm has been evaluated on dense phase contrast cellular data and the experimental results indicate that the proposed algorithm is able to accurately track epithelial and endothelial cells in time-lapse image sequences that are characterized by low contrast and high level of noise. Our algorithm achieved 86.10% overall tracking accuracy and 90.12% mitosis detection accuracy.

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