Parkinson's Disease Detection using Gray Level Spatial Dependance Matrix (GLSDM)

some years back, in the field of medical imaging system the Parkinson's disease segmentation in Parkinson's progression markers initiative (PPMI) has become an evolving research area. In the diagnosis of Parkinson's disease, precise exposure of size and location of affected area plays a vital role in field. In this era of several diagnostic and therapeutic applications, automatic defects detection in PPMI images is very vital. Parkinson's disease segmentation and seems very hard because of high quantity data in PPMI images and due to the boundaries which seem blurred. Classification of the tissues to three classes of normal, begin and malignant acts as a goal. The quantity of data is very high for manual interpretation and analysis in PPMI images.