Automated classification of multispectral MR images using unsupervised constrained energy minimization based on fuzzy logic.
Constrained energy minimization (CEM) has proven highly effective for hyperspectral (or multispectral) target detection and classification. It requires a complete knowledge of the desired target signature in images. This work presents "Unsupervised CEM (UCEM)," a novel approach to automatically target detection and classification in multispectral magnetic resonance (MR) images. The UCEM involves two processes, namely, target generation process (TGP) and CEM. The TGP is a fuzzy-set process that generates a set of potential targets from unknown information and then applies these targets to be desired targets in CEM. Finally, two sets of images, namely, computer-generated phantom images and real MR images, are used in the experiments to evaluate the effectiveness of UCEM. Experimental results demonstrate that UCEM segments a multispectral MR image much more effectively than either Functional MRI of the Brain's (FMRIB's) automated segmentation tool or fuzzy C-means does.
Lin
Geng-Cheng
GC
Department of Electrical Engineering, National Central University, Jhongli, Taiwan, ROC.
Wang
Chuin-Mu
CM
Wang
Wen-June
WJ
Sun
Sheng-Yih
SY
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D001932:Brain Neoplasms / Q000473:pathology*; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity