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, Volume 77, Issue 23, pp 30703–30727 | Cite as

A level set method based on local direction gradient for image segmentation with intensity inhomogeneity

  • Yingran Ma
  • Yanjun Peng
Article
  • 77 Downloads

Abstract

Many medical and real images are suffered from intensity inhomogeneity and weak edges. For higher image segmentation quality, lots of level set-based methods have been proposed. Some of them however cannot take advantage of image gradient information. And severe intensity inhomogeneity and weak edges are not disposed properly. To address these problems, a new level set method integrated with local direction gradient information is presented in this paper. Firstly, according to the two assumptions on image intensity inhomogeneity adopted by many existing methods, a new pixel classification model based on image gradient is introduced. Secondly, we employ variational level set method combined with image spatial information, which improves the anti-noise capability of the proposed method. Finally, considering the gray gradients in homogeneous regions are close to constants, an improved diffusion process is incorporated into the level set function to make the evolving curves stay around true image edges. To verify our method, different testing images including synthetic images, magnetic resonance imaging (MRI) and real-world images are introduced. The image segmentation results demonstrate that our method can deal with the relatively severe intensity inhomogeneity and obtain the comparatively ideal segmentation results efficiently.

Keywords

Image segmentation Image gradient Level set method Intensity inhomogeneity 

Notes

Acknowledgements

This work is supported by the National key research and development project of China under Grant No.2016YFC0801406, the Natural Science Foundation of Shandong Province under Grant No. ZR2015FM013, the National Natural Science Foundation of China under Grant No. 61502279, the National key research and development project of the Shandong Province under Grant No. 2016GSF120012, and by Special Project Fund of Taishan Scholars of Shandong Province, Leading Talent Project of Shandong University of Science and Technology.

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer Science and EngineeringShandong University of Science and TechnologyQingdaoChina
  2. 2.Shandong Province Key Laboratory of Wisdom Mining Information TechnologyShandong University of Science and TechnologyQingdaoChina

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