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U-Net Based Segmentation and Multiple Feature Extraction of Dermascopic Images for Efficient Diagnosis of Melanoma

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Computer Aided Intervention and Diagnostics in Clinical and Medical Images

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 31))

Abstract

Skin cancer is found to be one of the most common types of deadly cancers among human beings in recent years. Computational-based techniques are developed to support the dermatologists for the early diagnosis of skin cancer. Computational analysis of the skin lesions in the dermascopic images is a challenging task due to the difficulties such as low-level of contrast between the lesion and surrounding skin regions, irregular and vague lesion borders, artifacts and poor imaging conditions. This paper presents a U-Net based segmentation and multiple feature extraction of the dermascopic images for the efficient diagnosis of skin cancer. The input dermascopic image is preprocessed to remove the noise and hair in the skin image. Fast Independent Component Analysis (FastICA) is applied to the skin images for obtaining the melanin and hemoglobin components. The U-net segmentation is applied to the dermascopic image to separate the cancer region from the background of the skin image. Different features such as vascular features, color features, texture features, RGB features, and depth features are extracted from the segmented image. RVM classification is applied to classify the normal and abnormal images. With the efficient segmentation and extraction of multiple features, our proposed work yields better performance than the existing segmentation and feature extraction techniques.

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Authors and Affiliations

  1. Department of Information Technology, Sethu Institute of Technology, Pulloor, India

    D. Roja Ramani & S. Siva Ranjani

Authors
  1. D. Roja Ramani
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  2. S. Siva Ranjani
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Corresponding author

Correspondence to D. Roja Ramani .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India

    J. Dinesh Peter

  2. Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL, USA

    Steven Lawrence Fernandes

  3. Brazilian Computer Society, Aalcides Platiny Alves Batista , Porto Alegre, Rio Grande do Sul, Brazil

    Carlos Eduardo Thomaz

  4. School of Computer Science, University of KwaZulu-Natal, Durban, South Africa

    Serestina Viriri

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Roja Ramani, D., Siva Ranjani, S. (2019). U-Net Based Segmentation and Multiple Feature Extraction of Dermascopic Images for Efficient Diagnosis of Melanoma. In: Peter, J., Fernandes, S., Eduardo Thomaz, C., Viriri, S. (eds) Computer Aided Intervention and Diagnostics in Clinical and Medical Images. Lecture Notes in Computational Vision and Biomechanics, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-04061-1_9

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  • DOI: https://doi.org/10.1007/978-3-030-04061-1_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04060-4

  • Online ISBN: 978-3-030-04061-1

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