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Quantitative analysis in clinical applications of brain MRI using independent component analysis coupled with support vector machine

2010; Wiley; Volume: 32; Issue: 1 Linguagem: Inglês

10.1002/jmri.22210

1522-2586

Jyh‐Wen Chai, Clayton Chi‐Chang Chen, Chih‐Ming Chiang, Yung‐Jen Ho, Hsian‐Min Chen, Yen‐Chieh Ouyang, Ching‐Wen Yang, San‐Kan Lee, Chein‐I Chang,

Sparse and Compressive Sensing Techniques

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.