Automated classification of multispectral MR images using unsupervised constrained energy minimization based on fuzzy logic

NCUIR > College of Electrical Engineering & Computer Science > Department of Electrical Engineering > journal & Dissertation > Item 987654321/51917

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51917

Title:	Automated classification of multispectral MR images using unsupervised constrained energy minimization based on fuzzy logic
Authors:	Lin,GC;Wang,CM;Wang,WJ;Sun,SY
Contributors:	電機工程學系
Keywords:	ARTIFICIAL NEURAL-NETWORKS;MAGNETIC-RESONANCE IMAGES;SEGMENTATION TECHNIQUES;C-MEANS;BRAIN;ALGORITHM;MODEL
Date:	2010
Issue Date:	2012-03-28 10:10:22 (UTC+8)
Publisher:	國立中央大學
Abstract:	Constrained energy minimization (CEM) has proven highly effective for hyperspectral (or multispectral) target detection and classification. It requires a complete knowledge of the desired target signature in images. This work presents "Unsupervised CEM (UCEM)," a novel approach to automatically target detection and classification in multispectral magnetic resonance (MR) images. The UCEM involves two processes, namely, target generation process (TGP) and CEM. The TGP is a fuzzy-set process that generates a set of potential targets from unknown information and then applies these targets to be desired targets in CEM. Finally, two sets of images, namely, computer-generated phantom images and real MR images, are used in the experiments to evaluate the effectiveness of UCEM. Experimental results demonstrate that UCEM segments a multispectral MR image much more effectively than either Functional MRI of the Brain's (FMRIB's) automated segmentation tool or fuzzy C-means does. (C) 2010 Elsevier Inc. All rights reserved.
Relation:	MAGNETIC RESONANCE IMAGING
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

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