PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Colantonio S., Gurevich I. B., Salvetti O. A two-step approach for automatic microscopic image segmentation using fuzzy clustering and neural discrimination. In: Pattern Recognition and Image Analysis, vol. 17 (3) pp. 428 - 437. MAIK Nauka/Interperiodica distributed exclusively by Springer Science+Business Media LLC, 2007.
 
 
Abstract
(English)
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.
URL: http://www.springerlink.com/content/2234x73313141535/?p=12b14e0b1db74dfbb901c2a9fd9cf26e&pi=9
Subject Fuzzy Clustering
Neural Classification
Cytological Image Segmentation
I.4.6 Segmentation
I.5.3 Clustering
I.5.4 Pattern Recognition. Applications


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