Parkinson’s Disease MRIs Analysis Using Neutrosophic-Entropy Segmentation Approach

Brain MRIs are composed of three main regions such as gray matter, white matter and cerebrospinal fluid. Radiologists and medical practitioners make decisions through evaluating the developments in these regions. Study of these MRIs suffers from two major issues such as: (a) the boundaries of their gray matter and white matter regions are ambiguous and unclear in nature, and (b) their regions are formed with unclear inhomogeneous gray structures. These two issues make the diagnosis of critical diseases very complex. To solve these issues, this study presented a method of image segmentation based on the neutrosophic set (NS) theory and neutrosophic entropy information (NEI). By nature, the proposed method is adaptive to select the threshold value and is entitled as neutrosophic-entropy based adaptive thresholding segmentation algorithm (NEATSA). Experimental results, including statistical analyses showed that NEATSA can segment the main regions of MRIs very clearly compared to the well-known methods of image segmentation available in literature of pattern recognition and computer vision domains.