A Variational Level Set Model Based on Local Clustering for Image Segmentation

Abstract:

For images with intensity inhomogeneities that can’t get accurate segmentation results, this paper proposes a variational level set model based on local clustering. First,based on the model of images with intensity inhomogeneities, we use the K-mean clustering algorithm for intensity clustering in a neighborhood of each point of images with intensity inhomogeneities, and define a local clustering criterion function for the image intensities in the neighborhood. Then this local clustering criterion function is then integrated with respect to the neighborhood center to give a global criterion of image segmentation. This criterion defines an energy function as a local intensity fitting term in the level set model. By minimizing this energy, our method is able to get the accurate image segmentation. The image segmentation results prove that our model in the aspect of segmenting images with intensity inhomogeneity is better than piecewise constant (PC) models, and the segmentation efficiency is higher than region-scalable fitting (RSF) model.

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 4797-4801
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.4797
• Online since: May 2014
• Authors: Yu Zhou*, Wei Guo Zhang, Li Feng Li
• Keywords: Image Segmentation, Intensity Inhomogeneities, Local Clustering, MR, Variational Level Set

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