Improving tissue classification effects of brain MRI segmentation based on independent component analysis

San Kan Lee, Jyh Wen Chai, Clayton Chi Chang Chen, Hsian Min Chen, Shih Wei Wang, Yen Chieh Ouyang, Ching Wen Yang, Chein I. Chang

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper we develop a computer-aided brain tissues classification system for MR images and hence make possible for more accurate volume measurement on gray/ white matter, and cerebrospinal fluid. A correct classification of brain tissues is an important step in quantitative morphological study of brain. From the synthetic brain MR images experiment, it shows that using multispectral image processing technique, independent component analysis (ICA), and coupling with support vector machine (SVM) method can effectively classify brain tissues for brain MR images. In addition, we also demonstrated that the best performance can be achieved by using watershed algorithm as a pre-processing method for striping non-brain tissues. The Tanimoto index of GM/WM in synthetic MR images with noise level 0% and 3% are 0.82/0.89 and 0.73/0.80, separately which shows better performance than those what we have seen in the literatures.

Original languageEnglish
Pages (from-to)93-101
Number of pages9
JournalChinese Journal of Radiology
Volume34
Issue number2
Publication statusPublished - 2009 Jun

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

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