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Fast two-stage segmentation model for images with intensity inhomogeneity

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Abstract

Based on the local correntropy-based K-means clustering active contour model, this paper proposes a fast two-stage segmentation method for intensity inhomogeneous images. Under our framework, the segmentation process is split into two stages. In the first stage, we preliminary segment the down-sampled images by the proposed relaxed anisotropic–isotropic local correntropy-based K-means clustering (AILCK) model, which can obtain a coarse segmentation result quickly. Subsequently, in the second stage, we further segment original images by an improved AILCK model, where we use the up-sampled coarse contour obtained by the first stage as the initialization. Following it, to obtain the global minima of energy functions fast, we incorporate a weighted difference of anisotropic and isotropic total variations into relaxed formulation of the two-stage active contour models. And then, we minimize them utilizing the difference-of-convex algorithm and the primal–dual hybrid gradient method. The experimental results on synthetic and real-world images demonstrate that the proposed method can achieve accurate segmentation results for intensity inhomogeneous images in a fast way.

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Authors and Affiliations

  1. Department of Applied Mathematics, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, People’s Republic of China

    Yangyang Song & Guohua Peng

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  1. Yangyang Song
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  2. Guohua Peng
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Song, Y., Peng, G. Fast two-stage segmentation model for images with intensity inhomogeneity. Vis Comput 36, 1189–1202 (2020). https://doi.org/10.1007/s00371-019-01728-0

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