A new approach of brain MRI analysis for identifying Creutzfeldt-Jakob disease (CJD)

Creutzfeldt-Jakob disease (CJD) is an uncommon, degenerative, invariably destructive brain infection which is difficult to analyze, especially during its early stages. CJD is considered as prion infection and can do harm in the brain when it is developing as a misshapen form. By analyzing the characteristic of Magnetic Resonance Imaging (MRI) signal changes, prion disease can be identified. But till now in the diagnosis of Creutzfeldt-Jakob disease, MRI images are rather not utilized. By further processing brain MRI, advance CJD can be detected and pinpointed more efficiently. To have a mean of comparison, MRI scans of both a healthy patient and symptomatic patient are reviewed in this study. This paper is focused on CJD MRI analysis of head scans in order to determine caudate and putamen for identifying the existence of brain tissue using a new algorithm. To determine the difference of brain tissue between the MRI of healthy and CJD affected image with the help of pixel counting approach by analyzing the image block by block. The proposed algorithm performs to identify a CJD diseased image from a healthy image of brain MRI, image segmentation is partitioned by image preprocessing stage, image enhancement stage and pixel counting stage. To determine the parts of the brain affected by CJD, MRI scans of infected patients was taken as input. The healthy patients' scans are also later tested with the proposed program to check if the proposed algorithm produces any false positives or not.