A two-step approach for automatic microscopic image segmentation using fuzzy clustering and neural discrimination
Colantonio S., Gurevich I. B., Salvetti O.
Fuzzy Clustering Cytological Image Segmentation Computer Vision and Pattern Recognition Neural Classification Computer Graphics and Computer-Aided Design
The early diagnosis of lymphatic system tumors heavily relies on the computerized morphological analysis of blood cells in microscopic specimen images. Automating this analysis necessarily requires an accurate segmentation of the cells themselves. In this paper, we propose a robust method for the automatic segmentation of microscopic images. Cell segmentation is achieved following a coarse-to-fine approach, which primarily consists in the rough identification of the blood cell and, then, in the refinement of the nucleus contours by means of a neural model. The method proposed has been applied to different case studies, revealing its actual feasibility.
Source: Pattern recognition and image analysis 17 (2007): 428–437. doi:10.1134/S1054661807030108
Publisher: Distributed by Allen Press,, Lawrence, KS , Stati Uniti d'America
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@article{oai:it.cnr:prodotti:68398,
title = {A two-step approach for automatic microscopic image segmentation using fuzzy clustering and neural discrimination},
author = {Colantonio S. and Gurevich I. B. and Salvetti O.},
publisher = {Distributed by Allen Press,, Lawrence, KS , Stati Uniti d'America},
doi = {10.1134/s1054661807030108},
journal = {Pattern recognition and image analysis},
volume = {17},
pages = {428–437},
year = {2007}
}
0000-0003-2022-0804