Magnetic Resonance Imaging Processing Method Based on Linear Interpolation and Firefly Algorithm
DOI:
https://doi.org/10.52575/2687-0932-2025-52-2-400-412Keywords:
image processing, linear interpolation, firefly algorithm, convolutional neural network, histogram equalization, MRI image enhancementAbstract
The study is devoted to solving a pressing scientific problem, which consists in developing a method for improving the quality of magnetic resonance imaging (MRI) images taken to diagnose oncological diseases of the brain. In this case, the capabilities of neural network classifiers can be used to make diagnoses based on MRI images. One of the common methods traditionally used to solve the problem of improving image quality is the histogram equalization method. However, the analysis showed that images processed by this method are characterized by excessively high contrast and brightness, which can lead to a decrease in the accuracy of diagnosing brain tumors based on them. The authors propose a method for processing grayscale images, which is based on the combined use of linear interpolation and the firefly algorithm. To implement the proposed method for processing MRI images, an appropriate algorithm has been developed. The peak signal-to-noise ratio, contrast and brightness of images processed by the proposed method have been estimated. Experimental studies have shown that image processing by the proposed method can improve their quality. The use of MRI images processed by the proposed method makes it possible to increase the accuracy of neural network diagnostics of oncological diseases of the brain by 30 % compared to the use of images processed by the histogram equalization method.
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Аль Жанзир З.М. 2025. Улучшение изображений в оттенках серого путем разработки линейной интерполяции с использованием алгоритма Firefly. Современная наука: актуальные проблемы теории и практики. Серия: Естественные и технические науки, 3: 16–20.
Махди Т.Н., Игитян Е.В., Польщиков К.А., Корсунов Н.И. 2022. Оценивание эффективности функционирования диалоговой системы на основе применения нечеткого вывода с нейросетевой настройкой. Экономика. Информатика, 49(2): 356–374. DOI 10.52575/2687-0932-2022-49-2-356-374.
Польщиков К.А., Лазарев С.А., Константинов И.С., Польщикова О.Н., Свойкина Л.Ф., Игитян Е.В., Балакшин М.С. 2020. Модель для оценки эффективности выполнения робототехнической системой коммуникативных функций. СТИН, 6: 4–7.
Польщиков К.А., Польщикова О.Н., Игитян Е.В., Балакшин М.С. 2019. Алгоритм поддержки принятия решений по выбору средств обработки больших массивов естественно-языковых данных. Научные ведомости Белгородского государственного университета. Серия: Экономика. Информатика, 46(3): 553–562. DOI: 10.18413/2411-3808-2019-46-3-553-562.
Ясир М.Дж., Аль Жанзир З.М. 2025. Распознавание и классификация МРТ-изображений головного мозга с использованием нейронных сетей. Научный результат. Информационные технологии, 10(1): 77–85. DOI: 10.18413/2518-1092-2025-10-1-0-8.
Badža M.M., Barjaktarović M.Č. 2020. Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network. Applied Sciences, 10(6): 1999.
Brain tumors. 2025. Kaggle. URL: https://www.kaggle.com/datasets/thomasdubail/brain-tumors-256x256 (accessed: 12.05.2025).
Gómez-Guzmán M.A., Jiménez-Beristaín L., García-Guerrero E.E., López-Bonilla O.R., Tamayo-Perez U.J., Esqueda-Elizondo J.J., Palomino-Vizcaino K., Inzunza-González E. 2023. Classifying Brain Tumors on Magnetic Resonance Imaging by Using Convolutional Neural Networks. Electronics, 12(4): 955.
Gonzalez R. C., Woods R. E. 2018. Digital Image Processing. New York. Pearson. 1019 p.
Kumar V., Kumar D. 2021. A Systematic Review on Firefly Algorithm: Past, Present, and Future. Archives of Computational Methods in Engineering, 28: 3269–3291.
Mahdi T.N., Jameel J.Q., Polshchykov K.A., Lazarev S.А., Polshchykov I.K., Kiselev V.E. 2021. Clusters partition algorithm for a self-organizing map for detecting resource-intensive database inquiries in a geo-ecological monitoring system. Periodicals of Engineering and Natural Sciences, 9(4): 1138–1145. DOI: 10.21533/pen.v10i1.2584.
Musallam A.S., Sherif A.S., Hussein M.K. 2022. A New Convolutional Neural Network Architecture for Automatic Detection of Brain Tumors in Magnetic Resonance Imaging Images. IEEE Access, 10: 2775–2782.
Pan M.-S., Yang X.-L., Tang, J.-T. 2012. Research on Interpolation Methods in Medical Image Processing. Journal of Medical Systems, 36: 777–807.
Pannu A. 2015. Artificial Intelligence and its Application in Different Areas. International Journal of Engineering and Innovative Technology, 4(10): 79–84.
Polshchykov K.A., Velikanova A.S., Igityan E.V. 2022. Neural network natural language processing tools for identifying personal priorities in the project performer's selection in the field of smart agriculture. IOP Conference Series: Earth and Environmental Science, 1069(1): 012012.
Polshchykov K., Lazarev S., Kiselev V., Shabeeb A.H.T. 2021. Justification for the decision on loading channels of the network of geoecological monitoring of resources of the agroindustrial complex. Periodicals of Engineering and Natural Sciences, 9(3): 781–787.
Pour E.S., Zadeh Z.B. 2023. Brain Tumor Detection from MRI Images based on Cellular Neural Network and Firefly Algorithm. International Journal of Research in Engineering and Science, 11 (6): 609–618.
Sethi D., Bharti S., Prakash C. 2022. A comprehensive survey on gait analysis: History, parameters, approaches, pose estimation, and future work. Artificial Intelligence in Medicine, 129: 102314. DOI: 10.1016/j.artmed.2022.102314.
Singh U., Choubey M.K. 2021. A Review: Image Enhancement on MRI Images. 2021 5th International Conference on Information Systems and Computer Networks (ISCON), Mathura: 1–6.
Velikanova A.S., Polshchykov K.A., Likhosherstov R.V., Polshchykova A.K. 2021. The use of virtual reality and fuzzy neural network tools to identify the focus on achieving project results. Journal of Physics: Conference Series, 2060: 012017. DOI: 10.1088/1742-6596/2060/1/012017.
References
Al Janzeer Z.M. 2025. Enhancing Grayscale Images by Developing Linear Interpolation Using Firefly Algorithm. Modern Science: Current Problems of Theory and Practice. Series: Natural and Technical Sciences, 3: 16–20. (in Russian).
Mahdi T.N., Igityan E.V., Polshchikov K.A., Korsunov N.I. 2022. Evaluation of the Dialogue System Efficiency Based on the Application of Fuzzy Inference with Neural Network Settings. Economics. Information technologies, 49(2): 356–374 (in Russian). DOI: 10.52575/2687-0932-2022-49-2-356-374.
Pol'shhikov K.A., Lazarev S.A., Konstantinov I.S., Pol'shhikova O.N., Svojkina L.F., Igitjan E.V., Balakshin M.S. 2020. Model' dlja ocenki jeffektivnosti vypolnenija robototehnicheskoj sistemoj kommunikativnyh funkcij. STIN, 6: 4–7. (in Russian).
Polshchikov К.А., Polshchikova O.N., Igityan E.V., Balakshin M.S. 2019. The algorithm of decision support in the choice of means of processing large amounts of natural language data. Belgorod State University Scientific Bulletin. Economics. Information technologies, 46 (3): 553–562 (in Russian). DOI: 10.18413/2411-3808-2019-46-3-553-562.
Yaser M.J., Al Janzeer Z.M. 2025. Recognition and classification of MRI images of the brain using the neural networks. Research result. Information technologies, 10(1): 77–85. (in Russian). DOI: 10.18413/2518-1092-2025-10-1-0-8.
Badža M.M., Barjaktarović M.Č. 2020. Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network. Applied Sciences, 10(6): 1999.
Brain tumors. 2025. Kaggle. URL: https://www.kaggle.com/datasets/thomasdubail/brain-tumors-256x256 (accessed: 12.05.2025).
Gómez-Guzmán M.A., Jiménez-Beristaín L., García-Guerrero E.E., López-Bonilla O.R., Tamayo-Perez U.J., Esqueda-Elizondo J.J., Palomino-Vizcaino K., Inzunza-González E. 2023. Classifying Brain Tumors on Magnetic Resonance Imaging by Using Convolutional Neural Networks. Electronics, 12(4): 955.
Gonzalez R.C., Woods R.E. 2018. Digital Image Processing. New York. Pearson. 1019 p.
Kumar V., Kumar D. 2021. A Systematic Review on Firefly Algorithm: Past, Present, and Future. Archives of Computational Methods in Engineering, 28: 3269–3291.
Mahdi T.N., Jameel J.Q., Polshchykov K.A., Lazarev S.А., Polshchykov I.K., Kiselev V.E. 2021. Clusters partition algorithm for a self-organizing map for detecting resource-intensive database inquiries in a geo-ecological monitoring system. Periodicals of Engineering and Natural Sciences, 9(4): 1138–1145. DOI: 10.21533/pen.v10i1.2584.
Musallam A.S., Sherif A.S., Hussein M.K. 2022. A New Convolutional Neural Network Architecture for Automatic Detection of Brain Tumors in Magnetic Resonance Imaging Images. IEEE Access, 10: 2775–2782.
Pan M.-S., Yang X.-L., Tang, J.-T. 2012. Research on Interpolation Methods in Medical Image Processing. Journal of Medical Systems, 36: 777–807.
Pannu A. 2015. Artificial Intelligence and its Application in Different Areas. International Journal of Engineering and Innovative Technology, 4(10): 79–84.
Polshchykov K.A., Velikanova A.S., Igityan E.V. 2022. Neural network natural language processing tools for identifying personal priorities in the project performer's selection in the field of smart agriculture. IOP Conference Series: Earth and Environmental Science, 1069(1): 012012.
Polshchykov K., Lazarev S., Kiselev V., Shabeeb A.H.T. 2021. Justification for the decision on loading channels of the network of geoecological monitoring of resources of the agroindustrial complex. Periodicals of Engineering and Natural Sciences, 9(3): 781–787.
Pour E.S., Zadeh Z.B. 2023. Brain Tumor Detection from MRI Images based on Cellular Neural Network and Firefly Algorithm. International Journal of Research in Engineering and Science, 11 (6): 609–618.
Sethi D., Bharti S., Prakash C. 2022. A comprehensive survey on gait analysis: History, parameters, approaches, pose estimation, and future work. Artificial Intelligence in Medicine, 129: 102314. DOI: 10.1016/j.artmed.2022.102314.
Singh U., Choubey M.K. 2021. A Review: Image Enhancement on MRI Images. 2021 5th International Conference on Information Systems and Computer Networks (ISCON), Mathura: 1–6.
Velikanova A.S., Polshchykov K.A., Likhosherstov R.V., Polshchykova A.K. 2021. The use of virtual reality and fuzzy neural network tools to identify the focus on achieving project results. Journal of Physics: Conference Series, 2060: 012017. DOI: 10.1088/1742-6596/2060/1/012017.
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