%0 Journal Article
	%A Mohammad Javadi
	%D 2013
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 79, 2013
	%T A Serial Hierarchical Support Vector Machine and 2D Feature Sets Act for Brain DTI Segmentation
	%U https://publications.waset.org/pdf/16401
	%V 79
	%X Serial hierarchical support vector machine (SHSVM)
is proposed to discriminate three brain tissues which are white matter
(WM), gray matter (GM), and cerebrospinal fluid (CSF). SHSVM
has novel classification approach by repeating the hierarchical
classification on data set iteratively. It used Radial Basis Function
(rbf) Kernel with different tuning to obtain accurate results. Also as
the second approach, segmentation performed with DAGSVM
method. In this article eight univariate features from the raw DTI data
are extracted and all the possible 2D feature sets are examined within
the segmentation process. SHSVM succeed to obtain DSI values
higher than 0.95 accuracy for all the three tissues, which are higher
than DAGSVM results.

	%P 970 - 973