Improving the efficiency of brain MRI image analysis using feature selection
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 algo...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Samara National Research University
2022-08-01
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| Series: | Компьютерная оптика |
| Subjects: | |
| Online Access: | https://computeroptics.ru/eng/KO/Annot/KO46-4/460413e.html |
| _version_ | 1797653032999059456 |
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| author | V.V. Konevsky A.V. Blagov A.V. Gaidel A.V. Kapishnikov A.V. Kupriyanov E.N. Surovtsev D.G. Asatryan |
| author_facet | V.V. Konevsky A.V. Blagov A.V. Gaidel A.V. Kapishnikov A.V. Kupriyanov E.N. Surovtsev D.G. Asatryan |
| author_sort | V.V. Konevsky |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T16:38:37Z |
| format | Article |
| id | doaj.art-dcd9f2065f724089a0f769ee81667f35 |
| institution | Directory Open Access Journal |
| issn | 0134-2452 2412-6179 |
| language | English |
| last_indexed | 2024-03-11T16:38:37Z |
| publishDate | 2022-08-01 |
| publisher | Samara National Research University |
| record_format | Article |
| series | Компьютерная оптика |
| spelling | doaj.art-dcd9f2065f724089a0f769ee81667f352023-10-23T12:50:47ZengSamara National Research UniversityКомпьютерная оптика0134-24522412-61792022-08-0146462162710.18287/2412-6179-CO-1040Improving the efficiency of brain MRI image analysis using feature selectionV.V. Konevsky0A.V. Blagov1A.V. Gaidel2A.V. Kapishnikov3 A.V. Kupriyanov4E.N. Surovtsev5D.G. Asatryan6Samara National Research UniversitySamara National Research UniversityIPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS; Samara National Research UniversityFederal State Budgetary Educational Institution of Higher Education "Samara State Medical University" of the Minis-try of Health of the Russian FederationSamara National Research UniversityFederal State Budgetary Educational Institution of Higher Education "Samara State Medical University" of the Minis-try of Health of the Russian Federation Russian-Armenian University; Institute for Informatics and Automation Problems of National Academy of Sciences of ArmeniaThis 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.https://computeroptics.ru/eng/KO/Annot/KO46-4/460413e.htmltexture analysiscomputer opticsimage processinggreedy algorithmsmri diagnosticsmeningioma |
| spellingShingle | 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 Компьютерная оптика texture analysis computer optics image processing greedy algorithms mri diagnostics meningioma |
| title | Improving the efficiency of brain MRI image analysis using feature selection |
| title_full | Improving the efficiency of brain MRI image analysis using feature selection |
| title_fullStr | Improving the efficiency of brain MRI image analysis using feature selection |
| title_full_unstemmed | Improving the efficiency of brain MRI image analysis using feature selection |
| title_short | Improving the efficiency of brain MRI image analysis using feature selection |
| title_sort | improving the efficiency of brain mri image analysis using feature selection |
| topic | texture analysis computer optics image processing greedy algorithms mri diagnostics meningioma |
| url | https://computeroptics.ru/eng/KO/Annot/KO46-4/460413e.html |
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