A Comparative Assessment of Segmentations on Skin Lesion Through Various Entropy and Six Sigma Thresholds
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.
KeywordsImage 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.
- 1.Siegel RL, Miller KD, Jemal A (2017) Cancer statistics. CA Cancer J Clin 64(1):9–29Google Scholar
- 6.Soyer HP, Argenziano G, Chimenti S, Ruocco V (2001) Dermoscopy of pigmented skin lesions. Eur J Dermatol 11(3):270–277Google Scholar
- 7.Soyer HP, Argenziano G, Talamini R, Chimenti S (2001) Is dermoscopy useful for the diagnosis of melanoma? Arch Dermatol 137(10):1361–1363Google Scholar
- 8.Stolz W, Braun-Falco O, Bilek P, Landthaler M, Burgdorf WHC, Cognetta AB (eds) (2002) Color atlas of dermatoscopy. Wiley-Blackwell, HobokenGoogle Scholar
- 12.Mishra NK, Celebi ME (2016) An overview of melanoma detection in dermoscopy images using image processing and machine learning. arXiv preprint arXiv:1601.07843
- 13.Rosendahl et al (2012) Dermatoscopy in routine practice: ‘Chaos and clues’. Aust Fam Physician 41(7):482Google Scholar
- 14.Friedman RJ et al (1985) Early detection of malignant melanoma: the role of physician examination and self-examination of the skin. CA Cancer J Clin 35(3):130–151Google Scholar
- 22.Kaushik RHC et al. (2013) The median split algorithm for detection of critical melanoma color features. In: International conference on computer vision theory and applications (VISAPP), pp 492–495Google Scholar
- 25.Sabbaghi Mahmouei SA et al. (2015) An improved colour detection method in skin lesions using colour enhancement. In: Australian biomedical engineering conference (ABEC 2015)Google Scholar
- 26.Madooei A et al (2013) A colour palette for automatic detection of blue-white veil. In: Color and imaging conference, vol 2013, no 1, pp 200–205Google Scholar
- 27.Tiwari R, Sharma B (2016) A comparative study of Otsu and entropy based segmentation approaches for lesion extraction. In: Conference: 2016 international conference on inventive computation technologies (ICICT)Google Scholar
- 29.Comparison of Shannon, Renyi and Tsallis Entropy used in Decision Trees, Tomasz Maszczyk and Wlodzislaw DuchGoogle Scholar
- 32.Rényi A (1961) On measures of entropy and information. In: Proceedings of the fourth Berkeley symposium on mathematical statistics and probability, Volume 1: contributions to the theory of statistics, pp 547–561. University of California Press, Berkeley, California. http://projecteuclid.org/euclid.bsmsp/1200512181
- 34.Sankaran S, Sethumadhavan G (2013) Quantifications of asymmetries on the spectral bands of MALIGNANT melanoma using six sigma threshold as preprocessor. In: Third international conference on computational intelligence and information technology (CIIT 2013), Mumbai, pp 80–86. https://doi.org/10.1049/cp.2013.2575