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X-ray Image Contrast Enhancement Approach

Narzillo Mamatov“Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University,,Department of Digital technologies and artificial intelligence,Tashkent,UzbekistanKeulimjay Erejepov“Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University,,Department of Digital technologies and artificial intelligence,Tashkent,UzbekistanMalika Jalelova“Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University,,Department of Digital technologies and artificial intelligence,Tashkent,UzbekistanInomjon NarzullayevTashkent University of Information Technologies named after Muhammad al-Khwarizmi,Tashkent,UzbekistanAbdurashid Samijonov“Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University,,Department of Digital technologies and artificial intelligence,Tashkent,Uzbekistan
2024en
ABI

Аннотация

Contrast enhancement algorithms are important in improving the quality of X-ray images, helping medical professionals to diagnose and treat patients more accurately. Most of the contrast enhancement algorithms available today are applied to the entire image without taking into account local features and differences in contrast in different areas of the image. This can lead to undesirable consequences such as loss of detail in the dark areas of the image or saturation in the bright areas. In this regard, in this work, a contrast enhancement approach is proposed, which provides contrast enhancement in each fragment of the image, taking into account the local features of the X-ray image. This approach is based on dividing the image into small fragments and applying different contrast enhancement algorithms to each fragment. Each fragment was evaluated according to the RMS criterion, and the algorithm that gave the largest value was selected as the optimal one for this fragment. In addition, the research paper analyzed the effectiveness of the approach developed based on the test results of X-ray images.

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