Journal of Systems Architecture: the EUROMICRO Journal
Tuning range image segmentation by genetic algorithm
EURASIP Journal on Applied Signal Processing
Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines
Neural Computing and Applications
De-noising by soft-thresholding
IEEE Transactions on Information Theory
A genetic fuzzy rules learning approach for unseeded segmentation in echography
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
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Segmentation of lesions in ultrasound imaging is one of the key issues in the development of Computer Aided Diagnosis systems. This paper presents a hybrid solution to the segmentation problem. A linear filter composed of a Gaussian and a Laplacian of Gaussian filter is used to smooth the image, before applying a dynamic threshold to extract a rough segmentation. In parallel, a despeckle filter based on a Cellular Automata (CA) is used to remove noise. Then, an accurate segmentation is obtained applying the GrowCut algorithm, initialized from the rough segmentation, to the CA-filtered image. The algorithm requires tuning of several parameters, which proved difficult to obtain by hand. Thus, a Genetic Algorithm has been used to find the optimal parameter set. The fitness of the algorithm has been derived from the segmentation error obtained comparing the automatic segmentation with a manual one. Results indicate that using the GA-optimized parameters, the average segmentation error decreases from 5.75% obtained by manual tuning to 1.5% with GA-optimized parameters.