Classification of brain matters in MRI by Kernel Independent Component Analysis

Tomoko Tateyama, Zensho Nakao, Yen Wei Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

An Automatic segmentation system for MR imaging is neccessary for studies and 3 Dimentional visualization of anatomical structures in many clinical and research applications. Since conventional classification systems use a simple linear classifier, non-linear model is not taken into consideration. In this paper, we propose a new method based on Kernel Independent Component Analysis(KICA) for classification of phantom and clinical MR datasets. First, we extract kernel independent components from MR datasets by using KICA, and then the extracted components are used for classification of brain tissues. Since KICA, as a non-linear approach, can perform significant enhancement of brain MR datasets, the KICA-based classification method effectively classifies brain tissues and is computationally better than the conventional methods. The proposed method has been successfully applied to MR datasets and the classification performance has also been compared with conventional multi-spectral methods.

Original languageEnglish
Title of host publicationProceedings - 2008 4th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2008
Pages713-716
Number of pages4
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event2008 4th International Conference on Intelligent Information Hiding and Multiedia Signal Processing, IIH-MSP 2008 - Harbin, China
Duration: 15-08-200817-08-2008

Publication series

NameProceedings - 2008 4th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2008

Conference

Conference2008 4th International Conference on Intelligent Information Hiding and Multiedia Signal Processing, IIH-MSP 2008
Country/TerritoryChina
CityHarbin
Period15-08-0817-08-08

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Graphics and Computer-Aided Design
  • Signal Processing

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