Total variation and tight frame image segmentation with intensity inhomogeneity

Raymond Chan City University of Hong Kong Hongfei Yang The Chinese University of Hong Kong Tieyong Zeng The Chinese University of Hong Kong

Numerical Analysis and Scientific Computing mathscidoc:1904.25001

Image segmentation is an important task in the domain of computer vision and medical imaging. In natural and medical images, intensity inhomogeneity, i.e. the varying image intensity, occurs often and it poses considerable challenges for image segmentation. In this paper, we propose an efficient variational method for segmenting images with intensity inhomogeneity. The method is inspired by previous works on two-stage segmentation and variational Retinex. Our method consists of two stages. In the first stage, we decouple the image into reflection and illumination parts by solving a convex energy minimization model with either total variation or tight-frame regularisation. In the second stage, we segment the original image by thresholding on the reflection part, and the inhomogeneous intensity is estimated by the smoothly varying illumination part. We adopt a primal dual algorithm to solve the convex model in the first stage, and the convergence is guaranteed. Numerical experiments clearly show that our method is robust and efficient to segment both natural and medical images.
image segmentation, intensity inhomogeneity, primal-dual algorithm, Retinex, tight frame, total variation
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@inproceedings{raymondtotal,
  title={Total variation and tight frame image segmentation with intensity inhomogeneity},
  author={Raymond Chan, Hongfei Yang, and Tieyong Zeng},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20190410172021344412227},
}
Raymond Chan, Hongfei Yang, and Tieyong Zeng. Total variation and tight frame image segmentation with intensity inhomogeneity. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20190410172021344412227.
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