A Comparative Assessment of Segmentations on Skin Lesion Through Various Entropy and Six Sigma Thresholds

  • Srinivasan Sankaran
  • Jason R. Hagerty
  • Muthukumaran Malarvel
  • Gopalakrishnan SethumadhavanEmail author
  • William V. Stoecker
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


We present four entropy-based methods for colour segmentation within a lesion in a dermoscopy image for classification of the image as melanoma or benign. Four entropy segmentation methods are based on Tsallis, Havrda and Charvat, Renyi and Kapur entropy measures. Segmentation through Six Sigma threshold as preprocessor is also evaluated by this assessment approach. The proposed methods are inspired by two clinical observations about melanoma. First, colours within a lesion provide the most useful measures for melanoma detection; second, the disorder in colour variety and arrangement provides the best assessment of melanoma colours. These observations lead to the hypothesis that colour disorder is best measured by entropy. The five different models for colour splitting studied with SSIM measures taken from each region in the colour-split image for segmentation assessment. Based on the score helps to understand segmentation region assessment effectively.


Image analysis Melanoma Structural similarity measure Dermoscopy Image segmentation Information entropy Lesion segmentation Skin cancer 



This publication was made possible by SBIR Grants R43 CA153927-01 and CA101639-02A2 of the National Institutes of Health (NIH). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. Further, the authors would like to acknowledge the support rendered by the management of SASTRA Deemed to be University, Tirumalaisamudram, India.

This work is a by-product of generic image processing tool named “Bhadraloka” being developed (Dot Net platform) in SASTRA for the project titled “Development of techniques for processing radiographic images for automated detection of defects” with the funding assistance from Board of Research in Nuclear Science (BRNS), Department of Atomic Energy, Government of India (No. 2013/36/40-BRNS/2305).

Also, SS would like to thanks HCL Technologies Limited in supporting all through this research work.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Srinivasan Sankaran
    • 1
    • 3
  • Jason R. Hagerty
    • 2
  • Muthukumaran Malarvel
    • 1
  • Gopalakrishnan Sethumadhavan
    • 1
    Email author
  • William V. Stoecker
    • 2
  1. 1.SASTRA Deemed UniversityTirumalaisamudramIndia
  2. 2.S&A TechnologiesRollaUSA
  3. 3.HCL Technologies LimitedChennaiIndia

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