Authors:
Igor Gurevich
;
Artem Myagkov
;
Yuriy Sidorov
;
Yulia Trusova
and
Vera Yashina
Affiliation:
Russian Academy of Sciences, Russian Federation
Keyword(s):
Parkinson's disease, object detection, object identification, neuron, the substantia nigra pars compacta, arcuate nucleus of hypothalamus
Abstract:
This paper presents a new combined mathematical method, which were proposed, implemented, and experimentally tested for extracting information necessary for modeling and, in future, predicting Parkinson’s disease. The method was developed for extraction “neurons” from microscopic images of brain slices of experimental animals. Then it was adapted for different types of initial data, because unfortunately the quality of initial images depends on skills of the specialist who has done an experiment. Now 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 microscopi
c images. The method was applied to the following areas of brain: the substantia nigra pars compacta and the arcuate nucleus of hypothalamus.
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