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Type-2 Fuzzy Set Approach to Image Analysis

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Recent Trends on Type-2 Fuzzy Logic Systems: Theory, Methodology and Applications

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 425))

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Abstract

Segmentation is an essential task in image analysis process. Due to non-homogeneous intensities, blurred boundaries, noise and minimum of contrast it is a challenging task for image analysists. It has wide range of applications in all fields exclusively in the field medical imaging for disease deionization and early detection. The root cause for non-homogeneous intensities is uncertainty. Various tools have been introduced to handle uncertainty. We have introduced type-2 fuzzy based image segmentation process for edge detection in blurred areas of an image. When compared with classical fuzzy set, it has upper and lower membership values. Since it has more membership values it can handle higher level of uncertainty. In this chapter we have proposed equivalence function associated with strong negation relation which will address each intensity \({\mathcal{I}}_{t}\) of an image \(\mathcal{I}\) through the membership values. This method is verified with thermographic breast cancer image data set and the results were satisfactory.

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Authors and Affiliations

  1. Department of Mathematics, SRM Institute of Science and Technology-Ramapuram, Chennai, 600089, India

    K. Anitha

  2. Former Nuclear Scientist, Bhabha Atomic Research Centre, Mumbai, 400085, India

    Debabrata Datta

Authors
  1. K. Anitha
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  2. Debabrata Datta
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Correspondence to K. Anitha .

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Editors and Affiliations

  1. Research Chair of Graduate Studies, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  2. Department of Electronics and Communication Engineering, National Institute of Technology, Patna, India

    Anupam Kumar

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Anitha, K., Datta, D. (2023). Type-2 Fuzzy Set Approach to Image Analysis. In: Castillo, O., Kumar, A. (eds) Recent Trends on Type-2 Fuzzy Logic Systems: Theory, Methodology and Applications. Studies in Fuzziness and Soft Computing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-031-26332-3_12

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