DocumentCode :
2553133
Title :
Extension Artificial Immune System approach in MRI classification
Author :
Wang, Chuin-Mu ; Chu, Shao-Wei ; Su, Ching-Yuan
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Chin-Yi Univ. of Technol., Taichung, Taiwan
fYear :
2011
fDate :
21-25 June 2011
Firstpage :
855
Lastpage :
859
Abstract :
Magnetic Resonance Imaging (MRI) has become a useful modality because it provides unparallel capability of revealing soft tissue characterization as well as 3-D visualization. Immune system is regarded a remarkable mechanism capable of self-organizing to best strengthen its function for defending outside attacks. As such, the Artificial Immune System (AIS) theory is gradually adopted in designing optimal computation systems. In the study, an extension AIS(EAIS) shows antibody affinity to deal with enormous spectrum data and also characterizes Gray Matter (GM), White Matter (WM) and Cerebral Spinal Fluid (CSF) to highly benefit doctors and patients. According to the comparing results, the EAIS is better than C-means in classification.
Keywords :
artificial immune systems; biological tissues; biomedical MRI; brain; data visualisation; image classification; medical image processing; 3D visualization; AIS theory; MRI classification; antibody affinity; cerebral spinal fluid; extension AIS; extension artificial immune system; gray matter; magnetic resonance imaging; optimal computation system; outside attack; soft tissue characterization; spectrum data; white matter; Biomedical imaging; Cancer; Computer science; Correlation; Immune system; Magnetic resonance imaging; Artificial Immune System (AIS); Magnetic Resonance Imaging (MRI); classification; extension;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation (WCICA), 2011 9th World Congress on
Conference_Location :
Taipei
Print_ISBN :
978-1-61284-698-9
Type :
conf
DOI :
10.1109/WCICA.2011.5970636
Filename :
5970636
Link To Document :
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