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<p>Medical microscopical image captured using an optical microscope for medical purposes sometimes exhibit poor illumination and very little contrast. A new algorithm for enhancing microscopical images is suggested in this study. This algorithm depending on YIQ color space. Firstly, image enhancement is conducted using contrast-limited (AHE) Adapted Histogram Equalisation. Then, the chromatic components are isolated (HS) and the lightness component (Y) is enhanced using sigmoid algorithms with illuminance mapping. In this study, a method based on the separation of compounds in YIQ color space was proposed. The initial compounds were processed using sigmoid mapping. Then the image was enhanced using AHE and stretch. The proposed method is compared with several methods using microscopic data. The results show that the proposed algorithm excellent averages scales in terms of gradient mean values CEM (0.792), BRISQUE (29.592), and PIQE (39.379).</p>

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Improvement of medical microscopic images using nonlinear sigmoid mapping and adaptive histogram equalization

  • 期刊论文

  • 发表日期：

    2025年2月13日

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  • Shahad Ahmed Abd-Alameer,
  • Noor Jabbar Abraham,
  • Ghasaq Z. Alwan,
  • Hazim G. Daway

摘要

Medical microscopical image captured using an optical microscope for medical purposes sometimes exhibit poor illumination and very little contrast. A new algorithm for enhancing microscopical images is suggested in this study. This algorithm depending on YIQ color space. Firstly, image enhancement is conducted using contrast-limited (AHE) Adapted Histogram Equalisation. Then, the chromatic components are isolated (HS) and the lightness component (Y) is enhanced using sigmoid algorithms with illuminance mapping. In this study, a method based on the separation of compounds in YIQ color space was proposed. The initial compounds were processed using sigmoid mapping. Then the image was enhanced using AHE and stretch. The proposed method is compared with several methods using microscopic data. The results show that the proposed algorithm excellent averages scales in terms of gradient mean values CEM (0.792), BRISQUE (29.592), and PIQE (39.379).