Volume-based magnetic resonance brain image classification

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題名:	Volume-based magnetic resonance brain image classification
作者:	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.
貢獻者:	Department of Computer Science, Tunghai University
關鍵詞:	Fisher's linear discriminant analysis (FLDA);Iterative FLDA (IFLDA);Magnetic Resonance Image (MRI);Support Vector Machine (SVM);Volume Sphering Analysis (VSA)
日期:	2010-12-13
上傳時間:	2013-05-14T09:07:22Z (UTC)
出版者:	Shenzhen; China
摘要:	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.
關聯:	Proceedings - 4th International Conference on Genetic and Evolutionary Computing, ICGEC 2010 , art. no. 5715537 , pp. 739-742
顯示於類別:	[資訊工程學系所] 會議論文

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