Journal ArticleDOI
Current methods in medical image segmentation.
TLDR
A critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images is presented, with an emphasis on the advantages and disadvantages of these methods for medical imaging applications.Abstract:
▪ Abstract Image segmentation plays a crucial role in many medical-imaging applications, by automating or facilitating the delineation of anatomical structures and other regions of interest. We present a critical appraisal of the current status of semiautomated and automated methods for the segmentation of anatomical medical images. Terminology and important issues in image segmentation are first presented. Current segmentation approaches are then reviewed with an emphasis on the advantages and disadvantages of these methods for medical imaging applications. We conclude with a discussion on the future of image segmentation methods in biomedical research.read more
Citations
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Book ChapterDOI
Atlas guided identification of brain structures by combining 3d segmentation and SVM classification
TL;DR: A novel automatic approach for the identification of anatomical brain structures in magnetic resonance images (MRI) that combines a fast multiscale multi-channel three dimensional (3D) segmentation algorithm providing a rich feature vocabulary together with a support vector machine (SVM) based classifier.
Journal ArticleDOI
A comparison study of different image appraisal tools for electrical resistivity tomography
TL;DR: In this paper, the authors compare quantitatively different image appraisal indicators to detect artefacts, estimate depth of investigation, address parameters resolution and appraise ERT-derived geometry, and propose a methodology to appraise field ERT images.
Posted Content
A stochastic-variational model for soft Mumford-Shah segmentation
TL;DR: A stochastic-variational model for soft (or fuzzy) Mumford-Shah segmentation of mixture image patterns is proposed, which allows each pixel to belong to each image pattern with some probability.
Book ChapterDOI
3d kidney segmentation from CT images using a level set approach guided by a novel stochastic speed function
Fahmi Khalifa,Ahmed Elnakib,Garth M. Beache,Georgy Gimel'farb,M.A. El-Ghar,Rosemary Ouseph,Guela Sokhadze,Samantha Manning,Patrick McClure,Ayman El-Baz +9 more
TL;DR: A new 3-D segmentation approach for the kidney from computed tomography (CT) images that accounts for a shape prior and appearance features in terms of voxel-wise image intensities and their pair-wise spatial interactions integrated into a two-level joint Markov-Gibbs random field model of the kidney and its background.
Book ChapterDOI
Automatic extraction of femur contours from hip x-ray images
TL;DR: Experiments show that this model-based approach for automatically extracting femur contours from hip x-ray images can extract the contours of femurs with regular shapes, despite variations in size, shape and orientation.
References
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Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Journal ArticleDOI
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Book
Co-planar stereotaxic atlas of the human brain : 3-dimensional proportional system : an approach to cerebral imaging
TL;DR: Direct and Indirect Radiologic Localization Reference System: Basal Brain Line CA-CP Cerebral Structures in Three-Dimensional Space Practical Examples for the Use of the Atlas in Neuroradiologic Examinations Three- Dimensional Atlas of a Human Brain Nomenclature-Abbreviations Anatomic Index Conclusions.