Magnetic resonance brain tissue classification and volume calculation

• Published in: Journal of the Chinese Institute of Engineers Vol. 38; no. 8; pp. 1055 - 1066
• Main Authors: Chiou, Yaw-Jiunn, Chen, Clayton Chi-Chang, Chen, Shih-Yu, Chen, Hsian-Min, Chai, Jyh-Wen, Ouyang, Yen-Chieh, Su, Wu-Chung, Yang, Ching-Wen, Lee, San-Kan, Chang, Chein-I
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 17.11.2015
• Subjects: iterative Fisher's linear discriminant analysis (IFLDA); magnetic resonance image (MRI); support vector machine (SVM); volume sphering analysis (VSA)
• ISSN: 0253-3839; 2158-7299
• DOI: 10.1080/02533839.2015.1056552

This study develops a volume sphering analysis (VSA) approach to tissue classification and volume calculation of multispectral magnetic resonance (MR) brain images. It processes all multispectral MR image slices as an image cube while using only one set of training samples obtained from a single multispectral image slice to perform tissue classification as well as to calculate tissue volumes. In order to make a one slice set of training samples fit for all MR image slices a novel multispectral signature-specified extrapolation algorithm is particularly designed for this purpose so that the selected set of training samples can be extrapolated to create new data samples that are also applicable to other MR image slices. As a consequence, it significantly reduces the tremendous burden on radiologists for selection of training samples as well as computational cost. To further resolve instability and inconsistency issues which may be caused by training sample extrapolation, the proposed VSA also includes a support vector machine to refine training samples and develops an iterative Fisher's linear discriminant analysis (IFLDA) to make VSA robust and insensitive to new generated training samples so as to improve the traditional slice-by-slice MR image classification. Experimental results demonstrate that VSA in conjunction with IFLDA not only performs comparably to approaches using training samples from individual image slices, but also saves significant time in selecting training samples and computational cost.