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Skin Cancer Segmentation Based on Triangular Intuitionistic Fuzzy Sets

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A Correction to this article was published on 23 March 2023

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Abstract

Malignant Melanoma is a dangerous form of skin cancer, and its detection is a challenging task as it appears in numerous ranges of size, shape, and shading with various skin tones. Also, artefacts like hairs, outlines, blood vessels, and boils add further complexity. A simplified clustering method is proposed in this paper to improve melanoma detection while reducing time complexity.The triangular membership function (TMF) is used to detect the initial regions for obtaining initial centroids. These initial centroids are used to apply intuitionistic fuzzy c-means clustering. The TMF helps in identifying the initial clusters and regions and reduces the number of iterations needed for segmentation. The proposed method effectively detects skin cancer regions with an average accuracy of 90% and performs well.

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Authors and Affiliations

  1. School of Computer Science Engineering, VIT-AP University, Amaravati, Guntur, 522237, Andhra Pradesh, India

    Anupama Namburu, Sibi Chakkaravarthy & Prabha Selvaraj

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India

    Senthilkumar Mohan

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  1. Anupama Namburu
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  2. Senthilkumar Mohan
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  3. Sibi Chakkaravarthy
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  4. Prabha Selvaraj
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Correspondence to Anupama Namburu.

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This article is part of the topical collection “Advances in Applied Image Processing and Pattern Recognition” guest edited by KC Santosh.

The original online version of this article was revised: Due to incorrect affiliation of the second author. Now, it has been corrected.

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Namburu, A., Mohan, S., Chakkaravarthy, S. et al. Skin Cancer Segmentation Based on Triangular Intuitionistic Fuzzy Sets. SN COMPUT. SCI. 4, 228 (2023). https://doi.org/10.1007/s42979-023-01701-8

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