TY - JOUR
T1 - A novel method for magnetic resonance brain image classification based on adaptive chaotic PSO
AU - Zhang, Y.
AU - Wang, S.
AU - Wu, L.
PY - 2010
Y1 - 2010
N2 - Automated and accurate classification of magnetic resonance (MR) brain images is an integral component of the analysis and interpretation of neuroimaging. Many different and innovative methods have been proposed to improve upon this technology. In this study, we presented a forward neural network (FNN) based method to classify a given MR brain image as normal or abnormal. This method first employs a wavelet transform to extract features from images, and then applies the technique of principle component analysis (PCA) to reduce the dimensions of features. The reduced features are sent to an FNN, and these parameters are optimized via adaptive chaotic particle swarm optimization (ACPSO). K-fold stratified cross validation was used to enhance generalization. We applied the proposed method on 160 images (20 normal, 140 abnormal), and found that the classification accuracy is as high as 98.75% while the computation time per image is only 0.0452s.
AB - Automated and accurate classification of magnetic resonance (MR) brain images is an integral component of the analysis and interpretation of neuroimaging. Many different and innovative methods have been proposed to improve upon this technology. In this study, we presented a forward neural network (FNN) based method to classify a given MR brain image as normal or abnormal. This method first employs a wavelet transform to extract features from images, and then applies the technique of principle component analysis (PCA) to reduce the dimensions of features. The reduced features are sent to an FNN, and these parameters are optimized via adaptive chaotic particle swarm optimization (ACPSO). K-fold stratified cross validation was used to enhance generalization. We applied the proposed method on 160 images (20 normal, 140 abnormal), and found that the classification accuracy is as high as 98.75% while the computation time per image is only 0.0452s.
UR - http://www.scopus.com/inward/record.url?scp=78651253373&partnerID=8YFLogxK
U2 - 10.2528/PIER10090105
DO - 10.2528/PIER10090105
M3 - Article
AN - SCOPUS:78651253373
SN - 1070-4698
VL - 109
SP - 325
EP - 343
JO - Progress in Electromagnetics Research
JF - Progress in Electromagnetics Research
ER -