TY - GEN
T1 - Classification of Magnetic Resonance brain images by using weighted radial basis function kernels
AU - Tsai, Ching Tsorng
AU - Chen, Hsian Min
AU - Chai, Jyh Wen
AU - Chen, Clayton Chi Chang
AU - Chang, Chein I.
PY - 2011
Y1 - 2011
N2 - The paper proposed a weighted Radial basis function kernel (WRBF) approach that can be used to detect and classify anomalies in Magnetic Resonance (MR) images. A weighted Radial basis function kernel (WRBF) approach, despite the fact that the idea of WRBF kernels can be traced back to the work [1], its application to Radial basis function (RBF) kernel is new. It includes the Support Vector Machines (SVMs) using RBF as its special case where the RBF is considered to be uniformly weighted. Methods MR data of abnormal brain data were used to evaluate the accuracy of multiple sclerosis lesions classification by using the proposed method. The data were obtained from the BrainWeb Simulated Brain Database at the McConnell Brain Imaging Centre of the Montreal Neurological Institute (MNI), McGill University. Experimental results via various MR images show that WRBF kernels provide better classification.
AB - The paper proposed a weighted Radial basis function kernel (WRBF) approach that can be used to detect and classify anomalies in Magnetic Resonance (MR) images. A weighted Radial basis function kernel (WRBF) approach, despite the fact that the idea of WRBF kernels can be traced back to the work [1], its application to Radial basis function (RBF) kernel is new. It includes the Support Vector Machines (SVMs) using RBF as its special case where the RBF is considered to be uniformly weighted. Methods MR data of abnormal brain data were used to evaluate the accuracy of multiple sclerosis lesions classification by using the proposed method. The data were obtained from the BrainWeb Simulated Brain Database at the McConnell Brain Imaging Centre of the Montreal Neurological Institute (MNI), McGill University. Experimental results via various MR images show that WRBF kernels provide better classification.
UR - http://www.scopus.com/inward/record.url?scp=80955131926&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80955131926&partnerID=8YFLogxK
U2 - 10.1109/ICECENG.2011.6058066
DO - 10.1109/ICECENG.2011.6058066
M3 - Conference contribution
AN - SCOPUS:80955131926
SN - 9781424481637
T3 - 2011 International Conference on Electrical and Control Engineering, ICECE 2011 - Proceedings
SP - 5784
EP - 5787
BT - 2011 International Conference on Electrical and Control Engineering, ICECE 2011 - Proceedings
T2 - 2nd Annual Conference on Electrical and Control Engineering, ICECE 2011
Y2 - 16 September 2011 through 18 September 2011
ER -