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.
Automatic cell colony counting by region-growing approach / Masala, GL; Bottigli, U; Brunetti, A; Carpinelli, M; Diaz, N; Fiori, PL; Golosio, B; Oliva, P; Stegel, G. - In: IL NUOVO CIMENTO DELLA SOCIETÀ ITALIANA DI FISICA. C, GEOPHYSICS AND SPACE PHYSICS. - ISSN 1124-1896. - 30:6(2007), pp. 633-644. [10.1393/ncc/i2007-10273-3]
Automatic cell colony counting by region-growing approach
BRUNETTI, Antonio;CARPINELLI, Massimo;FIORI, Pier Luigi;OLIVA, Piernicola;STEGEL, Giovanni
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
