Abstract
A new combined mathematical method is proposed, implemented, and experimentally tested for extracting information necessary for modeling and, in future, predicting Parkinson's disease (PD) from microscopic images of brain slices of experimental animals. The method allows one to detect and identify as neurons a set of small informative extended objects with well distinguished (by brightness) oval inclusions. The result is a binary image of the contours of detected objects and their inclusions and a list of characteristics calculated for each detected object. The method is based on the joint application of image processing methods, methods of mathematical morphology, methods of segmentation, and the methods of classification of microscopic images.
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