{"id":2252,"date":"2025-05-13T19:06:28","date_gmt":"2025-05-13T18:06:28","guid":{"rendered":"http:\/\/newskbncran.ru\/?page_id=2252"},"modified":"2026-06-05T09:07:04","modified_gmt":"2026-06-05T08:07:04","slug":"26-6-4-en","status":"publish","type":"page","link":"http:\/\/izvestiyakbncran.ru\/index.php\/en\/26-6-4-en\/","title":{"rendered":"26.6.4 en"},"content":{"rendered":"\n

LUT generator based on smooth piecewise linear parameterized function<\/strong><\/h2>\n\n\n\n

M.A. Kazakov<\/strong><\/strong><\/p>\n\n\n\n

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Abstract.<\/em> <\/strong>When visualizing X-ray images, there is a problem of specific transformation of the original image in accordance with the examined tissues and body parts. Since different tissues have different ranges of densities, the required results can be achieved by expanding the dynamic range for pixels whose intensity values \u200b\u200bare in a certain range characteristic of the corresponding tissue. Such transformations for each pixel of the image are determined directly by the pixel intensity value and do not depend on the intensity values \u200b\u200bof neighboring pixels, i.e. the transformation is a function of one variable. This allows using tabular representations of functions (Lookup Tables \u2013 LUTs), which ensures high computational efficiency. To select the appropriate transformation functions, the participation of an expert is required, who will determine how correctly a particular function transforms the image. The paper presents a flexible generator of lookup tables (LUTs), with the help of which it is possible to produce tables with various parameters. Illustrations of the transformation results are provided. A link with an implementation in Python is attached, which provides the ability to visualize the result in order to interactively select the appropriate parameters.<\/p>\n\n\n\n

Keywords:<\/em> <\/strong>Lookup Tables, X-ray images, image processing, contrast enhancement<\/p>\n\n\n\n

For citation<\/strong>. Kazakov M.A. LUT generator based  on  smooth  piecewise  linear  parameterized  function.  News of the Kabardino-Balkarian Scientific Center of RAS.<\/em> <\/strong>2024. Vol. 26. No. 6. Pp. 45\u201352. DOI: 10.35330\/<\/strong>1991-6639-2024-26-6-45-52<\/p>\n\n\n\n

R<\/strong>eferences<\/summary>\n
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  1. Russo P. Handbook of X-ray imaging: physics and technology. CRC Press, 2018. 1419 p.<\/li>\n\n\n\n
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  6. Yoon D.C. et al. Digital radiographic image processing and analysis. Dental Clinics. 2018. Vol. 62. No. 3. Pp. 341\u2013359. DOI: 10.1016\/j.cden.2018.03.001<\/li>\n\n\n\n
  7. Freedman M.T., Artz D.S. Image processing in digital radiography. Seminars in roentgenology. WB Saunders. 1997. Vol. 32. No. 1. Pp. 25\u201337. DOI: 10.1016\/s0037-198x(97)80033-8<\/li>\n\n\n\n
  8. Carroll Q.B. Digital Radiography in Practice. Charles C Thomas Publisher, 2023.<\/li>\n\n\n\n
  9. Naidu S., Quadros A., Natekar A. et al. Enhancement of X-ray images using various Image Processing Approaches. International Conference on Technological Advancements and Innovations (ICTAI). Tashkent, 2021. Pp. 115\u2013120. DOI: 10.1109\/ICTAI53825.2021.9673317<\/li>\n\n\n\n
  10. Modi J. Look Up Tables in Radiography: Why Are They Useful? CE4RT.com, 1 Apr. 2023. https:\/\/ce4rt.com\/rad-tech-talk\/look-up-tables-in-radiography-what-are-they\/<\/li>\n\n\n\n
  11. Jo Y., Kim S.J. Practical single-image super-resolution using look-up table. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2021. Pp. 691\u2013700.<\/li>\n\n\n\n
  12. Kazakov M.A. A combined method for histogram equalization of high dynamic range images. News of the Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences. No. 6. Pp. 160\u2013166. DOI: 10.35330\/1991-6639-2023-6-116-160-166. (In Russian)<\/li>\n<\/ol>\n<\/details>\n\n\n\n
    Information about the author<\/strong><\/summary>\n
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    Mukhamed A. Kazakov<\/strong>, Junior Researcher of the Department of Neuroinformatics and Machine
    Learning, Institute of Applied Mathematics and Automation \u2013 branch of Kabardino-Balkarian Scientific
    Center of the Russian Academy of Sciences;
    360000, Russia, Nalchik, 89 A Shortanov street;
    kasakow.muchamed@gmail.com, ORCID: https:\/\/orcid.org\/0000-0002-5112-5079, SPIN-code: 6983-1220<\/p>\n\n\n\n

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    LUT generator based on smooth piecewise linear parameterized function M.A. Kazakov Abstract. When visualizing X-ray images, there is a problem of specific transformation of the original image in accordance with the examined tissues and body parts. Since different tissues have different ranges of densities, the required results can be achieved by expanding the dynamic range […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"wp-custom-template-home","meta":{"footnotes":""},"class_list":["post-2252","page","type-page","status-publish","hentry"],"yoast_head":"\n26.6.4 en - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"http:\/\/izvestiyakbncran.ru\/index.php\/en\/26-6-4-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"26.6.4 en - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb\" \/>\n<meta property=\"og:description\" content=\"LUT generator based on smooth piecewise linear parameterized function M.A. Kazakov Abstract. 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