The counting process of cell colonies is always a long and laborious process that is dependent on the judgment and ability of the operator. The judgment of the operator in counting can vary in relation to fatigue. Moreover, since this activity is time consuming it can limit the usable number of dishes for each experiment. For these purposes, it is necessary that an automatic system of cell colony counting is used. This article 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 algorithms of region-growing for the recognition of the regions of interest (ROI) in the image and a Sanger neural net for the characterization of such regions. The better final classification is supplied from a Feed-Forward Neural Net (FF-NN) and confronted,with the K-Nearest Neighbour (K-NN) and a Linear Discriminative Function (LDF). The preliminary results are shown.
Bottigli, U., Carpinelli, M., Fiori, P., Golosio, B., Marras, A., Masala, G., et al. (2006). A New Automatic System of Cell Colony Counting. In PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 15 (pp.159-163).
A New Automatic System of Cell Colony Counting
Carpinelli, M;
2006
Abstract
The counting process of cell colonies is always a long and laborious process that is dependent on the judgment and ability of the operator. The judgment of the operator in counting can vary in relation to fatigue. Moreover, since this activity is time consuming it can limit the usable number of dishes for each experiment. For these purposes, it is necessary that an automatic system of cell colony counting is used. This article 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 algorithms of region-growing for the recognition of the regions of interest (ROI) in the image and a Sanger neural net for the characterization of such regions. The better final classification is supplied from a Feed-Forward Neural Net (FF-NN) and confronted,with the K-Nearest Neighbour (K-NN) and a Linear Discriminative Function (LDF). The preliminary results are shown.
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