Feature Reduction and Texture Classification in MRI-Texture Analysis of Multiple Sclerosis

The aim of this study was to investigate the performance of texture analysis in texture classification and tissue discrimination between MS lesions, normal appearing white matter (NAWM) and normal white matter (NWM) in order to support early diagnosis of MS. T2-weighted MR images of sixteen relapsing remitting MS (RRMS) patients and sixteen healthy subjects were selected. Based on the lesion size, sixteen regions of interests (ROIs) were chosen from MS patient MR images and healthy subject MR images for MS lesions, NAWM and NWM respectively. Texture features extracted from grey level co-occurrence matrix (GLCM) were selected based on greatest feature difference. For statistical analysis, raw data analysis (RDA), principal component analysis (PCA) and nonlinear discriminant analysis (NDA) were applied to the texture features. The k-nearest neighbor (k-NN) and artificial neural network (ANN) methods were used for texture classification. Fisher coefficient and classification accuracy were used to evaluate the performance of texture analysis. The results demonstrated that (1) classification was successful (>90.00%) between MS lesions and NAWM or NWM, less successful (88.89%) among the three tissue groups and worst (66.67%) between NAWM and NWM; (2) In statistical analysis, NDA outperforms RDA and PCA; (3) ANN classified more accurately than k-NN method between NAWM and NWM, and among the three texture types. This study demonstrated that MRI texture analysis can achieve high classification accuracy in tissue discrimination between MS lesions and NAWM or NWM, which is valuable in supporting early diagnosis of MS.