On basic problems of image recognition in neurosciences and heuristic methods for their solution

• Published in: Pattern recognition and image analysis Vol. 25; no. 1; pp. 132 - 160
• Main Authors: Gurevich, I. B., Myagkov, A. A., Trusova, Yu. O., Yashina, V. V., Zhuravlev, Yu. I.
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 2015; Springer Nature B.V
• Subjects: Analysis; Applied Problems; Automation; Brain; Brain research; Cellular; Computer Science; Computer simulation; Diseases; Expenditures; Image analysis; Image Processing and Computer Vision; Image processing systems; Mathematical models; Medical imaging; Medical research; Nervous system; Neural networks; Neurodegeneration; Neurons; Neurosciences; Object recognition; Pattern Recognition; Science; Simulation; Software; Studies; neurodegenerative diseases; automation of biomedical image analysis; microscopical images of brain sections; microimages of neurons; biomedical image analysis; dophaminergic neurons; image recognition
• ISSN: 1054-6618; 1555-6212
• DOI: 10.1134/S105466181501006X

The paper describes the possibilities and main results of mathematical and informational approaches to automating the analysis, recognition, and evaluation of images in brain research. The latter are conducted in such essential sectors of neuroscience as molecular and cellular neuroscience, behavioral neuroscience, systemic neuroscience, developmental neuroscience, cognitive neuroscience, theoretical and computational neuroscience, neurology and psychiatry, neural engineering, neurolinguistics, and neurovisualization. An important direction in simulating diseases, including diseases of the brain and their diagnoses, is the obtaining, storage, processing, and analysis of data extracted from digital images. The theoretical and methodical basis of automating the processing, analysis, and evaluation of experimental data obtained in brain research consists of the mathematical theory of image recognition and mathematical theory of image analysis. The paper presents examples of mathematical and informational approaches to automate the processing, analysis, and evaluation of microimages of neurons for constructing preclinical models of Parkinson’s disease.