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Improving the efficiency of brain MRI image analysis using feature selection
V.V. Konevsky 1, A.V. Blagov 1, A.V. Gaidel 1,2, A.V. Kapishnikov 3, A.V. Kupriyanov 1, E.N. Surovtsev 3, D.G. Asatryan 4,5

1 Samara National Research University, 443086, Samara, Russia, Moskovskoye Shosse 34;
2 IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS,
443001, Samara, Russia, Molodogvardeyskaya 151;
3 Federal State Budgetary Educational Institution of Higher Education "Samara State Medical University" of the Minis-try of Health of the Russian Federation,
443099, Russia, Samara, st. Chapaevskaya, 89;
4 Russian-Armenian University, Armenia, Yerevan;
5 Institute for Informatics and Automation Problems of National Academy of Sciences of Armenia, Armenia, Yerevan

 PDF, 807 kB

DOI: 10.18287/2412-6179-CO-1040

Pages: 621-627.

Full text of article: Russian language.

Abstract:
This article discusses the possibility of improving the quality of analysis of MRI images of the brain in various scanning modes by using greedy feature selection algorithms. A total of five MRI sequences were reviewed. The texture features were formed using the MaZda software package. Using an algorithm for recursive feature selection, the accuracy of determining the type of tumor can be increased from 69% to 100%. With the help of the combined algorithm for the selection of signs, it was possible to increase the accuracy of determining the need for treatment of a patient from 60% to 75% and from 81% to 88% in the case of using an additional class of data for patients whose accurate result of treatment is unknown. The use of textural features in combination with a feature that is responsible for the type of meningioma made it possible to unambiguously determine the need for patient treatment.

Keywords:
texture analysis, computer optics, image processing, greedy algorithms, MRI diagnostics, meningioma.

Citation:
Konevsky VV, Blagov AV, Gaidel AV, Kapishnikov AV, Kupriyanov AV, Surovtsev EN, Asatryan DG. Improving the efficiency of brain MRI image analysis using feature selection. Computer Optics 2022; 46(4): 621-627. DOI: 10.18287/2412-6179-CO-1040.

Acknowledgements:
Theoretical studies were carried out with the support of the RFBR grant No. 19-29-01235 MK. The experimental results were obtained with the support of the Russian Foundation for Basic Research and RA Science Committee in the frames of the joint research project RFBR 20-51-05008 Аrm_a and SCS 20RF-144 accordingly.

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