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 automatic...

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Bibliographic Details
Published in:Magnetic resonance imaging 2010-06, Vol.28 (5), p.721-738
Main Authors: Lin, Geng-Cheng, Wang, Chuin-Mu, Wang, Wen-June, Sun, Sheng-Yih
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Brain - pathology
Brain Neoplasms - pathology
Classification
Constrained Energy Minimization (CEM)
FMRIB's Automated Segmentation Tool (FAST)
Fuzzy C-means
Fuzzy Logic
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Magnetic resonance imaging (MRI)
Multispectral
Pattern Recognition, Automated - methods
Radiology
Reproducibility of Results
Sensitivity and Specificity
Unsupervised
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Summary: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.
ISSN:0730-725X
1873-5894
DOI:10.1016/j.mri.2010.03.009