http://www.cnr.it/ontology/cnr/individuo/prodotto/ID68398
A two-step approach for automatic microscopic image segmentation using fuzzy clustering and neural discrimination (Articolo in rivista)
- Type
- Label
- A two-step approach for automatic microscopic image segmentation using fuzzy clustering and neural discrimination (Articolo in rivista) (literal)
- Anno
- 2007-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1134/S1054661807030108 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Colantonio S.; Gurevich I. B.; Salvetti O. (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
- In: Pattern Recognition and Image Analysis, vol. 17 (3) pp. 428 - 437. MAIK Nauka/Interperiodica distributed exclusively by Springer Science+Business Media LLC, 2007. (literal)
- Note
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Institute of Information Science and Technologies CNR, and Dorodnicyn Computing Center, Russian Academy of Sciences, ul. Vavilova 40, Moscow, 119991 Russia (literal)
- Titolo
- A two-step approach for automatic microscopic image segmentation using fuzzy clustering and neural discrimination (literal)
- Abstract
- 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. (literal)
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