title: Automated classification of multispectral MR images using unsupervised constrained energy minimization based on fuzzy logic.
abstract: 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.
authors.affiliation: Department of Electrical Engineering, National Central University, Jhongli, Taiwan, ROC.
authors.forename: Geng-Cheng
authors.initials: GC
authors.lastname: Lin
authors.forename: Chuin-Mu
authors.initials: CM
authors.lastname: Wang
authors.forename: Wen-June
authors.initials: WJ
authors.lastname: Wang
authors.forename: Sheng-Yih
authors.initials: SY
authors.lastname: Sun
mesh_terms: 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