detail on two of the most recent methods for medical image segmentation: (i) the Metamorphs, a semi-parametric deformable model that combines appearance and shape into a unified space, and (ii) the integration of geometric models with a collaborative formulation of CRFs in a probabilistic segmentation

of knowledge and a book in Her lotus hands. Dedicated to the Lotus Feet of the revered Sadguru. This thesis presents a new active mask (AM) framework and an algorithm for segmenta-tion of digital images, particularly those of punctate patterns from fluorescence microscopy. Fluorescence microscopy has greatly

unknown topology the level set framework was recently introduced to segment medical images. Unlike active contours, the level set method relies on an implicit shape representation rather than an explicit shape representation and hence new methods to impose prior knowledge about expected shape have

together in the multimedia system, computer-operated by the digitalized set-top box that opens up hundreds of channels of interactive communication, reaching from the global from the local. While there is oligopolistic concentration of multimedia groups, there is, at the same time, market segmentation

In medical image analysis strategies based on deformable models, controlling the de-formations of models is a desirable goal in order to produce proper segmentations. In teraction, global-to-local deformations, shape statistics, setting low-level parameters, and incorporating new forces or energy

Copyright © 2011 Mohammed Benjelloun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We propose a medical image segmentation

“Shape ” and “appearance”, the two pillars of a deformable model, complement each other in object segmentation. In many medical imaging applications, while the low-level appearance information is weak or mis-leading, shape priors play a more important role to guide a correct segmentation

various conditions and over time opens doors for spatiotemporal modeling of biological processes 1 . Quantification of the shapes and spatial distributions of subcellular objects is an important task, as the fluorescent probes are usually related to cellular markers. Colocalization of objects between

We 1 present a new method to shape-based segmentation of deformable anatomical structures in medical images and validate this approach by detecting and tracking the endocardial border in an echographic image sequence. To this end, a global prior knowledge of the endocardial contour is captured by a

Abstract: We present a new approach to shape-based segmentation and tracking of deformable anatomical structures in medical images, and validate this approach by detecting and tracking the endocardial contour in an echocardiographic image sequence. To this end, some global prior shape knowledge