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.
Segmentation of ultrasound breast images: optimization of algorithm parameters / Rogai, Francesco; Bocchi, Leonardo;. - STAMPA. - 6624:(2011), pp. 161-170. (Intervento presentato al convegno Applications of Evolutionary Computing, EvoApplications 2011 nel 2011-27-29 April).
Segmentation of ultrasound breast images: optimization of algorithm parameters
BOCCHI, LEONARDO
2011
Abstract
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.File | Dimensione | Formato | |
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