Quantitative analysis in clinical applications of brain MRI using independent component analysis coupled with support vector machine |
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| Authors: | Jyh‐Wen Chai MD PhD Clayton Chi‐Chang Chen MD Chih‐Ming Chiang MS Yung‐Jen Ho MD Hsian‐Min Chen PhD Yen‐Chieh Ouyang PhD Ching‐Wen Yang PhD San‐Kan Lee MD Chein‐I Chang PhD |
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| Affiliation: | 1. Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan;2. Section of Radiology, College of Medicine, China Medical University, Taichung, Taiwan;3. Department of Radiological Technology, Central Taiwan University of Science and Technology, Taichung, Taiwan;4. Department of Radiology, China Medical University Hospital, Taichung, Taiwan;5. Department of Biomedical Engineering, Hung Kuang University, Taichung, Taiwan;6. Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan;7. Computer Center, Taichung Veterans General Hospital, Taichung, Taiwan;8. Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, Maryland, USA |
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| Abstract: | ![]()
Purpose: To effectively perform quantification of brain normal tissues and pathologies simultaneously, independent component analysis (ICA) coupled with support vector machine (SVM) is investigated and evaluated for effective volumetric measurements of normal and lesion tissues using multispectral MR images. Materials and Methods: Synthetic and real MR data of normal brain and white matter lesion (WML) data were used to evaluate the accuracy and reproducibility of gray matter (GM), white matter (WM), and WML volume measurements by using the proposed ICA+SVM method to analyze three sets of MR images, T1‐weighted, T2‐weighted, and proton density/fluid‐attenuated inversion recovery images. Results: The Tanimoto indexes of GM/WM classification in the normal synthetic data calculated by the ICA+SVM method were 0.82/0.89 for data with 0% noise level. As for clinical MR data experiments, the ICA+SVM method clearly extracted the normal tissues and white matter hyperintensity lesions from the MR images, with low intra‐ and inter‐operator coefficient of variations. Conclusion: The experiments conducted provide evidence that the ICA+SVM method has shown promise and potential in applications to classification of normal and pathological tissues in brain MRI. J. Magn. Reson. Imaging 2010;32:24–34. © 2010 Wiley‐Liss, Inc. |
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| Keywords: | independent component analysis (ICA) support vector machine (SVM) brain MRI quantitative analysis |
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