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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Ballerini, L., Bocchi, L., Johansson, C.B. (2004). Image Segmentation by a Genetic Fuzzy c-Means Algorithm Using Color and Spatial Information. In: Raidl, G.R., et al. Applications of Evolutionary Computing. EvoWorkshops 2004. Lecture Notes in Computer Science, vol 3005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24653-4_27
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DOI: https://doi.org/10.1007/978-3-540-24653-4_27
Publisher Name: Springer, Berlin, Heidelberg
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