In this paper, we investigate the improvement obtained by applying a distributed genetic algorithm to a problem of parameter optimization in medical images analysis. We setup a method for the detection of clustered microcalcifications in digital mammograms, based on statistical techniques and multiresolution analysis by means of wavelet transform. The optimization of this scheme requires multiple runs on a set of 40 images, in order to obtain relevant statistics. We aim to evaluate how fluctuations of some parameters values of the detection method influence the performance of our system. A distributed genetic algorithm supervising this process allowed to improve of some percents previous results obtained after having "hand tuned" these parameters for a long time. At last, we have been able to find out parameters not influencing performance at all.
A distributed genetic algorithm for parameters optimization to detect microcalcifications in digital mammograms / Bevilacqua A.; Campanini R.; Lanconelli N.. - STAMPA. - 2037:(2001), pp. 278-287. (Intervento presentato al convegno The 3rd European Workshop on Evolutionary Computation in Image Analysis and Signal Processing (EVOIASP) tenutosi a Como, Italy nel April 18-20, 2001) [10.1007/3-540-45365-2_29].
A distributed genetic algorithm for parameters optimization to detect microcalcifications in digital mammograms
Bevilacqua A.;Lanconelli N.
2001
Abstract
In this paper, we investigate the improvement obtained by applying a distributed genetic algorithm to a problem of parameter optimization in medical images analysis. We setup a method for the detection of clustered microcalcifications in digital mammograms, based on statistical techniques and multiresolution analysis by means of wavelet transform. The optimization of this scheme requires multiple runs on a set of 40 images, in order to obtain relevant statistics. We aim to evaluate how fluctuations of some parameters values of the detection method influence the performance of our system. A distributed genetic algorithm supervising this process allowed to improve of some percents previous results obtained after having "hand tuned" these parameters for a long time. At last, we have been able to find out parameters not influencing performance at all.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
