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A Novel Image Segmentation Approach Based On Neutrosophic Set And Improved Fuzzy C-Means Algorithm

Author

Listed:
  • H. D. CHENG

    (School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China;
    Department of Computer Science, Utah State University, Logan, UT 84322, USA)

  • YANHUI GUO

    (Department of Computer Science, Utah State University, Logan, UT 84322, USA)

  • YINGTAO ZHANG

    (School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China)

Abstract

Image segmentation is an important component in image processing, pattern recognition and computer vision. Many segmentation algorithms have been proposed. However, segmentation methods for both noisy and noise-free images have not been studied in much detail.Neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interaction with different ideational spectra. However, neutrosophic set needs to be specified and clarified from a technical point of view for a given application or field to demonstrate its usefulness.In this paper, we apply neutrosophic set and define some operations. Neutrosphic set is integrated with an improved fuzzyc-means method and employed for image segmentation. A new operation, α-mean operation, is proposed to reduce the set indeterminacy. An improved fuzzyc-means (IFCM) is proposed based on neutrosophic set. The computation of membership and the convergence criterion of clustering are redefined accordingly. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can segment images accurately and effectively. Especially, it can segment the clean images and the images having different gray levels and complex objects, which is the most difficult task for image segmentation.

Suggested Citation

  • H. D. Cheng & Yanhui Guo & Yingtao Zhang, 2011. "A Novel Image Segmentation Approach Based On Neutrosophic Set And Improved Fuzzy C-Means Algorithm," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 7(01), pages 155-171.
    • Handle: RePEc:wsi:nmncxx:v:07:y:2011:i:01:n:s1793005711001858
    DOI: 10.1142/S1793005711001858
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    Cited by:

    1. Surapati Pramanik & Shyamal Dalapati & Shariful Alam & Florentin Smarandache & Tapan Kumar Roy, 2018. "NC-Cross Entropy Based MADM Strategy in Neutrosophic Cubic Set Environment," Mathematics, MDPI, vol. 6(5), pages 1-26, April.

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