Brain Tissue Classification Based on Diffusion Tensor Imaging: A Comparative Study Between Some Clustering Algorithms and Their Effect on Different Diffusion Tensor Imaging Scalar Indices

Abstract

Background: Brain segmentation from diffusion tensor imaging (DTI) into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) with acceptable results is subjected to many factors. Objectives: The most important issue in brain segmentation from DTI images is the selection of suitable scalar indices that best describe the required tissue in the images. Specifying suitable clustering method and suitable number of clusters of the selected method are other factors which affects the segmentation process significantly. Materials and Methods: The segmentation process is evaluated using four different clustering methods with different number of clusters where some DTI scalar indices for 10 human brains are processed. Results: The aim was to produce results with less segmentation error and a lower computational cost while attempting to minimizing boundary overlapping and minimizing the effect of artifacts due to macroscale scanning. Conclusion: The volume ratios of the best produced outputs with respect to the total brain size are 16.7% ± 3.53% for CSF, 35.05% ± 1.13% for WM, and 48.2% ± 2.88% for GM.