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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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Journal ArticleDOI

Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study.

TL;DR: This first study to investigate a trainable Weka segmentation (TWS) implementation using Random Forest machine-learning as a means to develop a fully automated tissue segmentation tool developed specifically for pediatric and adult examinations in a diagnostic CT environment investigates and optimizes a Random Forest algorithm for automated organ segmentation.
Journal ArticleDOI

Fuzzy C-means and region growing based classification of tumor from mammograms using hybrid texture feature

TL;DR: In the proposed CAD system, pre-processing is performed to suppress the noise in the mammographic image, then segmentation locates the tumor in mammograms using the cascading of Fuzzy C-Means and region-growing algorithm called FCMRG.
Journal ArticleDOI

The emerging discipline of Computational Functional Anatomy.

TL;DR: This work examines the transfer via these bijections of functional response variables into anatomical coordinates via group action on scalars and matrices in DTI as well as parallel transport of metric information across multiple templates which preserves the inner product.
Journal ArticleDOI

An adaptive differential evolution algorithm to optimal multi-level thresholding for MRI brain image segmentation

TL;DR: This paper proposes a DE solution that achieves a good balance between exploration and exploitation through a new adaptive approach and new mutation strategies, and experimentally compares the proposed DE algorithm, referred to as Adaptive Differential Evolution with Levy Distribution (ALDE), against three DE benchmark algorithms on T2 weighted MRI brain images.
Journal ArticleDOI

Pothole detection on asphalt pavements from 2D-colour pothole images using fuzzy c-means clustering and morphological reconstruction

TL;DR: The results of the study show that with well-planned road condition surveys, the proposed algorithm is suitable for the detection and extraction of incipient potholes from 2D vision images acquired using low-cost consumer-grade imaging sensors.
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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