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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 123 I-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.iii Acknowledgment First of all thanks to the Almighty for giving us the capability to conduct this research and finish our work successfully.Moreover, we would all like to thank our thesis supervisor DR. Md. Ashraful Alam for guiding us all along and working as the main driving force whenever we lacked anything in our work. He kept his door open to us to assist with any problems, big or small, and enlightened us with his immense knowledge on our concerned topic. Furthermore, sincere thanks to BRAC University for creating an entire thesis lab which is well facilitated with sufficient computers, fast internet and all equipment needed for our work. This lab provided all of our team members the opportunity to work together whenever we had to work as a team or work separately.We would also like to thank the Parkinson"s Progress Marker"s Initiative (PPMI) for letting us use their data to conduct our study successfully.Last but not the least, our sincere gratitude to every faculty members who assisted us in making ourselves capable enough throughout our university life and also to all the staffs of our university who are a part of creating a wonderful environment here that contributed to a good and healthy university life.iv

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