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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.

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Citations
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Proceedings ArticleDOI

An intelligent system for segmenting an abdominal image in multi core architecture

TL;DR: A method for kidney segmentation from an abdominal image is proposed and it suggest the idea about segmentation of multiple regions like Spine, Kidney, Liver of Abdominal image in order to produce efficient and prompt result.
Proceedings ArticleDOI

Medical image segmentation by improved 3D adaptive thresholding

TL;DR: The Improved Adaptive Segmentation Algorithm is suggested which can perform the segmentation process which is the most important process at making 3D model.
Book ChapterDOI

Überatlas: Robust Speed-Up of Feature-Based Registration and Multi-Atlas Segmentation

TL;DR: This paper presents a novel feature-based registration method for affine registration that succeeds in producing better and more robust segmentation results compared to two baseline methods, one intensity-based and one feature- based, and significantly reduces the running times.
Journal ArticleDOI

Riches of phenotype computationally extracted from microbial colonies

TL;DR: A suite of automated image processing, feature extraction, visualization, and classification algorithms (MORPHE) that facilitated the analysis of heterochromatin dynamics in the context of colonial growth and that can be broadly adapted to many colony-based assays in Saccharomyces and other microbes are developed.
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

Simon Haykin
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

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.
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