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Computer Optics, 2022, Volume 46, Issue 4, Pages 621–627
DOI: https://doi.org/10.18287/2412-6179-CO-1040 (Mi co1053) 		 
IMAGE PROCESSING, PATTERN RECOGNITION

Improving the efficiency of brain MRI image analysis using feature selection

V. V. Konevskya, A. V. Blagova, A. V. Gaidelab, A. V. Kapishnikovc, A. V. Kupriyanova, E. N. Surovtsevc, D. G. Asatryande

a Samara National Research University
b Image Processing Systems Institute of the RAS - Branch of the FSRC "Crystallography and Photonics" RAS, Samara, Russia, Samara
c Samara State Medical University
d Russian-Armenian University, Yerevan
e Institute for Informatics and Automation Problems of National Academy of Science of the Republic of Armenia
Full-text PDF (788 kB)
DOI: https://doi.org/10.18287/2412-6179-CO-1040
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
Funding agency Grant number
Russian Foundation for Basic Research 19-29-01235 МК
20-51-05008 Арм_а
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.
Received: 03.09.2021
Accepted: 21.11.2021
Document Type: Article
Language: Russian
Citation: V. V. Konevsky, A. V. Blagov, A. V. Gaidel, A. V. Kapishnikov, A. V. Kupriyanov, E. N. Surovtsev, D. G. Asatryan, “Improving the efficiency of brain MRI image analysis using feature selection”, Computer Optics, 46:4 (2022), 621–627
Citation in format AMSBIB
\Bibitem{KonBlaGai22}
\by V.~V.~Konevsky, A.~V.~Blagov, A.~V.~Gaidel, A.~V.~Kapishnikov, A.~V.~Kupriyanov, E.~N.~Surovtsev, D.~G.~Asatryan
\paper Improving the efficiency of brain MRI image analysis using feature selection
\jour Computer Optics
\yr 2022
\vol 46
\issue 4
\pages 621--627
\mathnet{http://mi.mathnet.ru/co1053}
\crossref{https://doi.org/10.18287/2412-6179-CO-1040}
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