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Automated Segmentation of Skin Lesions Using Seed Points and Scale-Invariant Semantic Mathematic Model

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Proceedings of the International Conference on Soft Computing Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 397))

Abstract

A color image-based segmentation method for segmenting skin lesions is proposed in this paper. This proposed methodology mainly includes two parts: First, a combination of scale-invariant and semantic mathematic model is utilized to classify different pixels. Second, a strategy based on skeleton corner point’s extraction is proposed in order to extract the seed points for the skin lesion image. By this method, the skin slices are processed in series automatically. As a result, the lesions present in the skin can be segmented clearly and accurately. The proposed algorithm is trained and tested for 360 skin slices in order to evaluate the accuracy of segmentation. Overall accuracy of the proposed method is compared with existing conventional techniques. An average missing pixel rate of 3.02 % and faulting pixel rate or 2.36 % has been obtained for segmenting the skin lesion images.

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Authors and Affiliations

  1. Department of Computer and Network Engineering, College of Engineering, Shaqra University, Al Dawadmi, Kingdom of Saudi Arabia

    Z. Faizal Khan

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  1. Z. Faizal Khan
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Correspondence to Z. Faizal Khan .

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Editors and Affiliations

  1. Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu, India

    L. Padma Suresh

  2. IIT Delhi, New Delhi, Delhi, India

    Bijaya Ketan Panigrahi

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Faizal Khan, Z. (2016). Automated Segmentation of Skin Lesions Using Seed Points and Scale-Invariant Semantic Mathematic Model. In: Suresh, L., Panigrahi, B. (eds) Proceedings of the International Conference on Soft Computing Systems. Advances in Intelligent Systems and Computing, vol 397. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2671-0_21

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  • DOI: https://doi.org/10.1007/978-81-322-2671-0_21

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2669-7

  • Online ISBN: 978-81-322-2671-0

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