TOPOGRAPHIC MAP BASED SEGMENTATION OF MEDICAL IMAGES
Keywords:
medical image, topographic map, CLAHE, enhancementAbstract
Today, diagnosis based on automatic processing of medical data is one of the
main areas of medicine. Particularly, it continues to play a crucial role in the processing,
segmentation, and analysis of medical images used in the identification of certain diseases.
Obtaining medical images based on high energy in MRT, endoscopy, X-ray, and ultrasound
examinations provides good quality, but an increase in the amount of high energy can reach
specific organs of a person. Low-energy imaging generates poor image quality. It might be
challenging or less accurate to diagnose from such poor-quality images. Nowadays, computer
technologies are used to solve such problems, and based on their processing, quick diagnoses are
made with the help of identifying the necessary areas, preliminary auxiliary diagnosis, and most
importantly, the amount of damage and pain caused to the patient's human organs is reduced. In
this abstract, the segmentation based on the topographic map method is considered. The CLAHE
algorithm is used to increase image quality, and the topographic map method is used for image
segmentation.
References
Грузман И.С., Киричук В.С., Косых В.П., Перетягин Г.И. и др. Цифровая обработка изображений в информационных системах. – Новосибирск: Изд-во НГТУ, 2002. – 352 с.
Бойко Д.А., Филатова А.Е. Метод повышения качества визуализации рентгенологических изображений // Вестник Национального технического университета «Харьковский политехнический институт». Серия: Информатика и моделирование. – 2015. – № 32. – С. 19–26.
Gardezi S.J., Elazab A., Lei B., Wang T. Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review // Journal of Medical Internet Research. – 2019. – Vol. 21, No. 7. – Article e14464.
Fazilov S., Abdieva Kh., Yusupov O., Eshonqulov E., Malikov Z. Re-Parameterized Gompertz Algorithm for the Fuzzy Ranks of Deep CNN Models in the Identification of Breast Cancer Using Mammography Images // AIP Conference Proceedings. – 2024. – Vol. 3244. – Article 030083. DOI: .
Fazilov S.K., Yusupov O.R., Abdiyeva K.S. An Algorithm for Evaluating the Parameters Characterizing the Quality of the Image Field of the Iris by the Principal Component Analysis Method // 2022 International Conference on Information Science and Communications Technologies (ICISCT). – Tashkent, Uzbekistan, 2022. – P. 1–4. DOI: .
Suganya R., Rajaram S., Abdullah A.S. Big Data in Medical Image Processing. – Oxford: Taylor & Francis, 2018.
Singh H. Practical Machine Learning and Image Processing. – New York: Springer Science & Business Media, 2019.