Paper addresses the problems of histological image segmentation and morphometric feature extraction. Manual analysis of histological images is time-consuming and prone to subjective errors, making automation highly relevant. A novel algorithm is proposed, which includes threshold segmentation based on Otsu's method and connected component labeling. The developed algorithm enables automatic segmentation of cell nuclei and other structures, as well as calculation of morphometric parameters such as area, perimeter. Experimental results demonstrate significant improvements in segment homogeneity, contrast and boundary quality. The algorithm is characterized by ease of use, high processing speed, and adaptability to different types of histological specimens.
| Mualliflar | Мелиев, Фарход, Meliyev, F.M., Abbasova, M.M. |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2026-05-30 |
| Son | 2 |
| Betlar | 212-219 |
| Til | Rus |
histological images, segmentation, morphometric features, Otsu method, connected component labeling (CCL), cell nuclei, area, perimeter, shape coefficient, digital pathology., гистологические изображения, сегментация, морфометрические признаки, метод Оцу, выделение связных компонентов (CCL), ядра клеток, площадь, периметр, коэффициент формы, цифровая патология., gistologik tasvirlar, segmentatsiya, morfometrik belgilar, Otsu usuli, bog‘langan komponentlarni belgilash (CCL), hujayra yadrolari, yuza, perimetr, shakl koeffitsiyenti, raqamli patologiya.
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