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Volume 24, Issue 3
Image Segmentation by Piecewise Constant Mumford-Shah Model Without Estimating the Constants

Xue-Cheng Tai & Chang-Hui Yao

J. Comp. Math., 24 (2006), pp. 435-443.

Published online: 2006-06

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  • Abstract

In this work, we try to use the so-called Piecewise Constant Level Set Method (PCLSM) for the Mumford-Shah segmentation model. For image segmentation, the Mumford-Shah model needs to find the regions and the constant values inside the regions for the segmentation. In order to use PCLSM for this purpose, we need to solve a minimization problem using the level set function and the constant values as minimization variables. In this work, we test on a model such that we only need to minimize with respect to the level set function, i.e., we do not need to minimize with respect to the constant values. Gradient descent method and Newton method are used to solve the Euler-Lagrange equation for the minimization problem. Numerical experiments are given to show the efficiency and advantages of the new model and algorithms.

  • Keywords

PCLSM, Image Segmentation, Mumford-Shah model.

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{JCM-24-435, author = {Xue-Cheng Tai and Chang-Hui Yao}, title = {Image Segmentation by Piecewise Constant Mumford-Shah Model Without Estimating the Constants}, journal = {Journal of Computational Mathematics}, year = {2006}, volume = {24}, number = {3}, pages = {435--443}, abstract = {

In this work, we try to use the so-called Piecewise Constant Level Set Method (PCLSM) for the Mumford-Shah segmentation model. For image segmentation, the Mumford-Shah model needs to find the regions and the constant values inside the regions for the segmentation. In order to use PCLSM for this purpose, we need to solve a minimization problem using the level set function and the constant values as minimization variables. In this work, we test on a model such that we only need to minimize with respect to the level set function, i.e., we do not need to minimize with respect to the constant values. Gradient descent method and Newton method are used to solve the Euler-Lagrange equation for the minimization problem. Numerical experiments are given to show the efficiency and advantages of the new model and algorithms.

}, issn = {1991-7139}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jcm/8764.html} }
TY - JOUR T1 - Image Segmentation by Piecewise Constant Mumford-Shah Model Without Estimating the Constants AU - Xue-Cheng Tai & Chang-Hui Yao JO - Journal of Computational Mathematics VL - 3 SP - 435 EP - 443 PY - 2006 DA - 2006/06 SN - 24 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jcm/8764.html KW - PCLSM, Image Segmentation, Mumford-Shah model. AB -

In this work, we try to use the so-called Piecewise Constant Level Set Method (PCLSM) for the Mumford-Shah segmentation model. For image segmentation, the Mumford-Shah model needs to find the regions and the constant values inside the regions for the segmentation. In order to use PCLSM for this purpose, we need to solve a minimization problem using the level set function and the constant values as minimization variables. In this work, we test on a model such that we only need to minimize with respect to the level set function, i.e., we do not need to minimize with respect to the constant values. Gradient descent method and Newton method are used to solve the Euler-Lagrange equation for the minimization problem. Numerical experiments are given to show the efficiency and advantages of the new model and algorithms.

Xue-Cheng Tai and Chang-Hui Yao. (2006). Image Segmentation by Piecewise Constant Mumford-Shah Model Without Estimating the Constants. Journal of Computational Mathematics. 24 (3). 435-443. doi:
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