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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Journal ArticleDOI
Brain MRI segmentation by combining different MRI modalities using Dempster–Shafer theory
Fattane Tavakoli,Jamal Ghasemi +1 more
TL;DR: It is proposed that the Dempster–Shafer theory and fuzzy clustering can be combined for brain MRI segmentation because of their robustness and improved 3–4% over that of the previous methods which had showed the best results.
Book ChapterDOI
Depth Estimation - An Introduction
TL;DR: The depth estimation strategies section will detail, analyze and present results of the main families of algorithms which solve the depth estimation problem, among them, the stereo vision based approaches.
Journal ArticleDOI
Automatic roi extraction in noisy medical images
S. Renukalatha,K. V. Suresh +1 more
TL;DR: An automatic Region of Interest (ROI) extraction algorithm to detect the important regions in noisy medical images of different modalities using statistical moments using histogram decomposition technique is proposed.
Posted Content
MR Acquisition-Invariant Representation Learning.
TL;DR: A proof of principle is provided, which shows that a linear classifier applied on the MRAI representation is able to outperform supervised convolutional neural network classifiers for tissue classification when little target training data is available.
Journal ArticleDOI
Improved Detection of the First-Order Region for Direction-Finding HF Radars Using Image Processing Techniques
TL;DR: In this paper, a marker-controlled watershed segmentation technique was proposed to separate areas of spectral energy due to surface currents from areas of energy due by more complex scattering by the wave field.
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.