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Image Segmentation Based on Fractional Differentiation and RSF Model

[+] Author Affiliations
Guimei Zhang, Yangang Zhu

Nanchang Hangkong University, Nanchang, China

Jianxin Liu

Xihua University, Sichuan, China

YangQuan Chen

University of California Merced, Merced, CA

Paper No. DETC2017-67110, pp. V009T07A023; 7 pages
doi:10.1115/DETC2017-67110
From:
  • ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 9: 13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications
  • Cleveland, Ohio, USA, August 6–9, 2017
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5823-3
  • Copyright © 2017 by ASME

abstract

Intensity inhomogeneity or weak texture region image segmentation plays an important role in computer vision and image processing. RSF (Region-Scalable Fitting) active contour model has been proved to be an effective method to segment intensity inhomogeneity. However RSF model is sensitive to the initial location of evolution curve , it tends to fall into local optimal. Aiming at the problem, this paper proposed a new method for image segmentation based on fractional differentiation and RSF model. The proposed method adds the global Grünwald-Letnikov fractional gradient into the RSF model. Thus the gradient of the intensity inhomogeneity and weak texture regions is strengthened. As a result, both the robustness of initial location of evolution curve and efficiency of image segmentation are improved. Theoretical analysis and experimental results demonstrate that the proposed algorithm is capable of segmenting the intensity inhomogeneities and weak texture images. It is robust to curve initial location, furthermore the efficiency of segmentation is improved.

Copyright © 2017 by ASME

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