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Open Access

Entropy-Based Global and Local Weight Adaptive Image Segmentation Models

Taiyuan University of Technology, Taiyuan 030024, China.
College of Computer Science and Engineering, Northeastern University, Shengyang 110819, China.
Shanxi Tizones Technology Co. Ltd., Taiyuan 030082, China.
Shanxi Taisen Technology Co. Ltd., Taiyuan 030082, China.
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Abstract

This paper proposes a parameter adaptive hybrid model for image segmentation. The hybrid model combines the global and local information in an image, and provides an automated solution for adjusting the selection of the two weight parameters. Firstly, it combines an improved local model with the global Chan-Vese (CV) model , while the image’s local entropy is used to establish the index for measuring the image’s gray-level information. Parameter adjustment is then performed by the real-time acquisition of the ratio of the different functional energy in a self-adapting model responsive to gray-scale distribution in the image segmentation process. Compared with the traditional linear adjustment model, which is based on trial-and-error, this paper presents a more quantitative and intelligent method for achieving the dynamic nonlinear adjustment of global and local terms. Experiments show that the proposed model achieves fast and accurate segmentation for different types of noisy and non-uniform grayscale images and noise images. Moreover, the method demonstrates high stability and is insensitive to the position of the initial contour.

Keywords

image segmentationimage local entropyparameter adaptionactive contour

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Tsinghua Science and Technology
Volume 25 Issue 1,
February 2020
Pages 149-160
DOI: 10.26599/TST.2019.9010026
Cite this article:
Li G, Zhao Y, Zhang L, et al. Entropy-Based Global and Local Weight Adaptive Image Segmentation Models. Tsinghua Science and Technology, 2020, 25(1): 149-160. https://doi.org/10.26599/TST.2019.9010026

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Received: 11 May 2019
Accepted: 05 June 2019
Published: 22 July 2019
© The author(s) 2020

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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