TY - GEN
T1 - A supervised hybrid classifier for brain tissues and white matter lesions on multispectral MRI
AU - Chen, Hsian Min
AU - Wang, Hsin Che
AU - Chang, Yung Chieh
AU - Chai, Jyh Wen
AU - Chen, Clayton Chi Chang
AU - Chang, Chein I.
AU - Hung, Che Lun
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/27
Y1 - 2017/11/27
N2 - Accurate quantification of brain tissues is a very challenging problem in neuroimaging, such as quantification of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF), and white matter lesions (WMLs). However, on many cases brain tissues and white matter lesions cannot be segmented and separated simultaneously by current techniques. Recently, a TRIO algorithm (TRIOA) is proposed to integrate three algorithms, Independent Component Analysis (ICA), Support Vector Machine (SVM) and Fisher's Linear Discriminant Analysis (FLDA) (ICA+SVM+IFLDA) which can effectively classify GM, WM and CSF by considering MR images as multispectral images in the native coordinate space. This paper further extends TRIOA in conjunction with Band Expansion Process (BEP), called Extended TRIOA (ETRIOA), to classify brain tissues as well as WMLs simultaneously. The accuracy assessment of the ETRIOA was evaluated by using the similarity index. The conducted experimental results demonstrate the clinical applicability of ETRIOA in simultaneous classification of GM, WM, CSF and WMLs.
AB - Accurate quantification of brain tissues is a very challenging problem in neuroimaging, such as quantification of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF), and white matter lesions (WMLs). However, on many cases brain tissues and white matter lesions cannot be segmented and separated simultaneously by current techniques. Recently, a TRIO algorithm (TRIOA) is proposed to integrate three algorithms, Independent Component Analysis (ICA), Support Vector Machine (SVM) and Fisher's Linear Discriminant Analysis (FLDA) (ICA+SVM+IFLDA) which can effectively classify GM, WM and CSF by considering MR images as multispectral images in the native coordinate space. This paper further extends TRIOA in conjunction with Band Expansion Process (BEP), called Extended TRIOA (ETRIOA), to classify brain tissues as well as WMLs simultaneously. The accuracy assessment of the ETRIOA was evaluated by using the similarity index. The conducted experimental results demonstrate the clinical applicability of ETRIOA in simultaneous classification of GM, WM, CSF and WMLs.
KW - Band expansion process (BEP)
KW - Brain MRI
KW - Multispectral MRI
KW - TRIOA
UR - http://www.scopus.com/inward/record.url?scp=85048267064&partnerID=8YFLogxK
U2 - 10.1109/ISPAN-FCST-ISCC.2017.54
DO - 10.1109/ISPAN-FCST-ISCC.2017.54
M3 - Conference contribution
AN - SCOPUS:85048267064
T3 - Proceedings - 14th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2017, 11th International Conference on Frontier of Computer Science and Technology, FCST 2017 and 3rd International Symposium of Creative Computing, ISCC 2017
SP - 375
EP - 379
BT - Proceedings - 14th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2017, 11th International Conference on Frontier of Computer Science and Technology, FCST 2017 and 3rd International Symposium of Creative Computing, ISCC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2017, 11th International Conference on Frontier of Computer Science and Technology, FCST 2017 and 3rd International Symposium of Creative Computing, ISCC 2017
Y2 - 21 June 2017 through 23 June 2017
ER -