TY - GEN
T1 - Classification of brain matters in MRI by Kernel Independent Component Analysis
AU - Tateyama, Tomoko
AU - Nakao, Zensho
AU - Chen, Yen Wei
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=54049091196&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=54049091196&partnerID=8YFLogxK
U2 - 10.1109/IIH-MSP.2008.240
DO - 10.1109/IIH-MSP.2008.240
M3 - Conference contribution
AN - SCOPUS:54049091196
SN - 9780769532783
T3 - Proceedings - 2008 4th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2008
SP - 713
EP - 716
BT - Proceedings - 2008 4th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2008
T2 - 2008 4th International Conference on Intelligent Information Hiding and Multiedia Signal Processing, IIH-MSP 2008
Y2 - 15 August 2008 through 17 August 2008
ER -