Non-Convex and Convex Coupling Image Segmentation via TGpV Regularization and Thresholding

Non-Convex and Convex Coupling Image Segmentation via TGpV Regularization and Thresholding

Year:    2020

Author:    Tingting Wu, Jinbo Shao

Advances in Applied Mathematics and Mechanics, Vol. 12 (2020), Iss. 3 : pp. 849–878

Abstract

In this paper, we propose a non-convex and convex coupling variational model for image segmentation. We design the non-convex and convex regularization terms based on total generalized p-variation (TGpV) regularizer to preserve the boundary of segmented parts and detect the structure in the image. Our method has two stages. The first stage is to approximate the Mumford-Shah model. The second stage is to segment the smoothed $u$ into different phases by using a thresholding strategy. We develop a scheme based on the alternating direction method of multipliers (ADMM) algorithm,  generalized $p$-shrinkage operation and K-means clustering method to carry out our method. We perform numerical experiments on many kinds of images such as real Bacteria image, Tubular magnetic resonance angiography (MRA) image, magnetic resonance (MR) images, anti-mass images, artificial images, noisy or blurred images. Some comparisons are arranged to show the effectiveness and advantages of our method.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/aamm.OA-2019-0199

Advances in Applied Mathematics and Mechanics, Vol. 12 (2020), Iss. 3 : pp. 849–878

Published online:    2020-01

AMS Subject Headings:    Global Science Press

Copyright:    COPYRIGHT: © Global Science Press

Pages:    30

Keywords:    Two-stage strategy non-convex and convex coupling total generalized $p$-variation (TGpV) alternating direction method of multipliers (ADMM) clustering methods.

Author Details

Tingting Wu

Jinbo Shao

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