A computer vision approach to diagnose Parkinson Disease using Brain CT Images

Parkinson disease is a disease that affects the central nervous system and creates a degenerative disorder of the central nervous system. Motor system is affected due to disorder in central nervous system of the brain. The clinical diagnosis of the Parkinson disease result in excess medical cost, errors and does not provide correct detection. The symptoms of the disease are slowness, rigidity, shaking and difficult in walking. By analyzing the current condition of the brain dopamine nerve terminal, one can successfully predict the odds of being affected by this deadly disease. The proposed methodology to detect the Parkinson disease utilizes a special algorithm for image processing of brain CT images. The part of the brain that is affected from Parkinson disease is differentiated from the non Parkinson disease affected part of the brain. The proposed algorithm is robust, gender and age independent. It automatically predicts whether or not a person has Parkinson disease by taking a real time input from the user.