Identification of Parkinson’s Disease using OpenCV
Early detection of Parkinson‟s Disease (PD) is very crucial for effective management and treatment of the disease. Dopaminergic images such as Single Photon Emission Tomography (SPECT) using 123I-Ioflupane can substantially detect Parkinson‟s Disease at an early stage. However, till today, these images are mostly interpreted by humans which can manifest interobserver variability and inconsistency. To improve the imaging diagnosis of PD, we propose a model in this paper, for early detection of Parkinson‟s disease using Image Processing and Artificial Neural Network (ANN). The model used 200 SPECT images, 100 of healthy normal and 100 of PD, obtained from Parkinson‟s Progression Marker‟s Initiative (PPMI) database and processed them to find the area of Caudate and Putamen which is the Region of Interest (ROI) for this study. The area values were then fed to the ANN which is hypothesized to mimic the pattern recognition of a human observer. The simple but fast ANN built, could classify subjects with and without PD with an accuracy of 94%, sensitivity of 100% and specificity of 88%. Hence it can be inferred that the proposed system has the potential to be an effective way to aid the clinicians in the accurate diagnosis of Parkinson‟s disease.
Parkinson‟s disease (PD) is a long term degenerative disorder of the Central Nervous System which causes a diverse set of symptoms ranging from tremor to cognitive impairment, hallucination, dementia, sleep disorders etc. More than 10 million people in the world suffer with Parkinson‟s disease . In Bangladesh, every year, approximately 1600 people die from Parkinson Disease. Till now there is no cure for PD . However about ten years before the onset of tremor or motor symptoms, the dopaminergic neurons begin to change. Some premotor symptoms of Parkinson‟s disease (at an early stage) includes decrease in sense of smell, disorder in Rapid Eye Movement (REM) sleep, small handwriting, difficulty in moving etc. An early diagnosis of PD results in effective management and avoidance of unnecessary medical tests, therapies, costs, safety risks etc. Parkinson‟s at an early stage is most commonly detected using brain scans such as MRI, fMRI, SPECT, PET etc. Till today, in most clinics, these images are interpreted by clinicians; with added possibility of human error. In a study it was reported that the pooled accuracy of clinical diagnosis of Parkinson‟s disease is only 80.6% . This motivated us to come up with a new and simple approach to detect Parkinson‟s disease at an early stage automatically using Image Processing and Artificial Neural Network. Adapting this technology in hospitals or diagnostic centers can increase accuracy in diagnosis of Parkinson‟s and save money and time.
- Thresholding method
- K means clustering
- Manual analysis – time consuming, inaccurate and requires intensive trained person to avoid diagnostic errors.
- Difficult to get accurate results
- Not applicable for multiple images for Tumor detection in a short time
- Medical Resonance images contain a noise caused by operator performance which can lead to serious inaccuracies classification.
Proposed System The proposed system follows several steps. Firstly, the required data are collected from PPMI database. Then the images are preprocessed, and then the region of interest of our work is segmented from the processed image and detected. The area is calculated from the current image and is fed as the input of neural network. Now the prediction model is obtained in which the subjects are tested for acquiring the desired result that whether the patient has PD or is normal.
Detecting Parkinson’s Disease
- It can segment the Parkinson‟s Disease regions from the image accurately.
- It is useful to classify the Parkinson‟s Disease images for accurate detection.
- Parkinson‟s Disease will be detected in an early stages
- Parkinson‟s Disease diagnosis system for medical application
- Python Idle
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