This paper describes a new clustering algorithm for color image segmentation. We combine the classical fuzzy c-means algorithm (FCM) with a genetic algorithm (GA), and we modify the objective function of the FCM for taking into account the spatial information of image data and the intensity inhomogeneities. An application to medical images is presented. Experiments show that the proposed algorithm provides a useful method for image segmentation, without the need of a prefiltering step for background estimation. Moreover, the segmentation of noise images is effectively improved
Image Segmentation by a GeneticFuzzy c-Means Algorithm Using Color and Spatial Information / L. Ballerini; L. Bocchi; C.B. Johansson. - STAMPA. - (2004), pp. 260-269. (Intervento presentato al convegno EvoIASP 2004) [10.1007/b96500].
Image Segmentation by a GeneticFuzzy c-Means Algorithm Using Color and Spatial Information
BOCCHI, LEONARDO;
2004
Abstract
This paper describes a new clustering algorithm for color image segmentation. We combine the classical fuzzy c-means algorithm (FCM) with a genetic algorithm (GA), and we modify the objective function of the FCM for taking into account the spatial information of image data and the intensity inhomogeneities. An application to medical images is presented. Experiments show that the proposed algorithm provides a useful method for image segmentation, without the need of a prefiltering step for background estimation. Moreover, the segmentation of noise images is effectively improved
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