Automatic cell colony counting by region-growing approach

Masala, G.L., Bottigli, U., Brunetti, A., Carpinelli, M., Diaz, N, Fiori, P.L., Golosio, B., Oliva, P., Stegel, G. (2007) Automatic cell colony counting by region-growing approach. Il Nuovo Cimento della Societa Italiana di Fisica C, 30 (6). pp. 633-644. ISSN 2037-4909. E-ISSN 1826-9885. (doi:10.1393/ncc/i2007-10273-3) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:91927)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL: http://dx.doi.org/10.1393/ncc/i2007-10273-3

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 FeedForward Neural Net (FF-NN) and compared with the K-Nearest Neighbour (K-NN). The results on large dataset of ROIs are shown.

Item Type:	Article
DOI/Identification number:	10.1393/ncc/i2007-10273-3
Uncontrolled keywords:	Nuclear medicine imaging; Image analysis
Subjects:	Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Institutional Unit:	Schools > School of Computing
Former Institutional Unit:	Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User:	Amy Boaler
Date Deposited:	02 Dec 2021 13:47 UTC
Last Modified:	20 May 2025 10:26 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/91927 (The current URI for this page, for reference purposes)

University of Kent Author Information

Masala, G.L..

Creator's ORCID:	https://orcid.org/0000-0001-6734-9424
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