This paper introduces a new automatic system, of counting based on the elaboration of the digital images of cellular colonies grown on petri dishes. This system is mainly based on the region-growing algorithms for the recognition of the Regions Of Interest (ROI) in the image and Sanger's neural network for the characterization of such regions. Moreover a recognition of the most important filters is made in alternative respect; to region-growing approach. The new Graphics Users Interface is introduced. The better final classification is supplied from a Feed-Forward Neural Net (FF-NN) and compared with the K-Nearest Neighbour (K-NN). The results oil large dataset of ROIs are shown.

Masala, G.L., Bottigli, U., Brunetti, A., Carpinelli, M., Diaz, N., Fiori, P.L., et al. (2007). Automatic cell colony counting by region-growing approach. IL NUOVO CIMENTO DELLA SOCIETÀ ITALIANA DI FISICA. C, GEOPHYSICS AND SPACE PHYSICS, 30(6), 633-644 [10.1393/ncc/i2007-10273-3].

Automatic cell colony counting by region-growing approach

U. Bottigli;
2007

Abstract

This paper introduces a new automatic system, of counting based on the elaboration of the digital images of cellular colonies grown on petri dishes. This system is mainly based on the region-growing algorithms for the recognition of the Regions Of Interest (ROI) in the image and Sanger's neural network for the characterization of such regions. Moreover a recognition of the most important filters is made in alternative respect; to region-growing approach. The new Graphics Users Interface is introduced. The better final classification is supplied from a Feed-Forward Neural Net (FF-NN) and compared with the K-Nearest Neighbour (K-NN). The results oil large dataset of ROIs are shown.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/409486