A new computer vision-based approach to aid the diagnosis of Parkinson's disease. (November 2016) - Pereira, Clayton R.; Pereira, Danilo R.; Silva, Francisco A.; Masieiro, João P.; Weber, Silke A.T.; Hook, Christian; Papa, João P.
Highlights: To develop a new dataset based on handwritten exams to aid Parkinson's Disease diagnosis. To propose a new approach to extract features from those exams based on image processing. To evaluate the features extracted from the dataset over two different drawings: spirals and meanders. To make available the dataset to the scientific community. Abstract: Background and Objective: Even today, pointing out an exam that can diagnose a patient with Parkinson's disease (PD) accurately enough is not an easy task. Although a number of techniques have been used in search for a more precise method, detecting such illness and measuring its level of severity early enough to postpone its side effects are not straightforward. In this work, after reviewing a considerable number of works, we conclude that only a few techniques address the problem of PD recognition by means of micrography using computer vision techniques. Therefore, we consider the problem of aiding automatic PD diagnosis by means of spirals and meanders filled out in forms, which are then compared with the template for feature extraction. Methods: In our work, both the template and the drawings are identified and separated automatically using image processing techniques, thus needing no user intervention. Since we have no registered images, the idea is to obtain a suitable representation of both template and drawings using the very same approach for all images in a fast and accurate approach. Results: The results have shown that we can obtain very reasonable recognition rates (around ≈67%), with the most accurate class being the one represented by the patients, which outnumbered the control individuals in the proposed dataset. Conclusions: The proposed approach seemed to be suitable for aiding in automatic PD diagnosis by means of computer vision and machine learning techniques. Also, meander images play an important role, leading to higher accuracies than spiral images. We also observed that the main problem in detecting PD is the patients in the early stages, who can draw near-perfect objects, which are very similar to the ones made by control patients.