Volume-based magnetic resonance brain image classification

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Please use this identifier to cite or link to this item: http://140.128.103.80:8080/handle/310901/21484

Title:	Volume-based magnetic resonance brain image classification
Authors:	Chiou, Y.-J.,?Chai, J.-W.,?Chen, C.C.-C.,?Chen, S.-Y., ?Chen, H.-M.,?Ouyang, Y.-C.,?Su, W.-C.,?Tsai, C.T.,?Yang, C.-W.,?Lee, S.-K.,?Chang, C.-I.
Contributors:	Department of Computer Science, Tunghai University
Keywords:	Fisher's linear discriminant analysis (FLDA);Iterative FLDA (IFLDA);Magnetic Resonance Image (MRI);Support Vector Machine (SVM);Volume Sphering Analysis (VSA)
Date:	2010-12-13
Issue Date:	2013-05-14T09:07:22Z (UTC)
Publisher:	Shenzhen; China
Abstract:	This paper develops a volume-based technique, called Volume Sphering Analysis (VSA) which can process all acquired Magnetic Resonance (MR) image slices-formed image cube using only one set of training samples obtained from an image slice. So, several significant advantages and benefits can be gained from our proposed VSA. In the past, when MR image classification is performed, each image slice requires its own specific training samples and training samples obtained from one slice are not applied to another slice. The VSA allows users to reduce computational time. In addition, it saves significant effort in selecting training samples for each of image slices. Thirdly, it is robust to all image slices compared to the traditional one-slice MR image classification which is sensitive to each image slice. Experimental results demonstrate that the VSA performs as well as does that using specifically selected training samples for individual image slices. ? 2010 IEEE.
Relation:	Proceedings - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010 , art. no. 5715537 , pp. 739-742
Appears in Collections:	[資訊工程學系所] 會議論文

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