Skin cancer remains among the deadliest forms of cancer, with an average survival rate ranging from 18% to 20%. Detecting and segmenting melanoma early is a challenging yet crucial undertaking. To effectively segment the lesions, several studies have presented automated and standard techniques. Likewise, traditional methods for segmentation frequently require inputs from humans and cannot be applied in automated systems. The aim of this article is to provide a comparative analysis of a various number of segmentation methods. The methods are explained along with their benefits and drawbacks. In numerous studies, effective methods for image segmentation of skin lesions have been successfully explored. Therefore, based on an analysis of the various segmentation approaches, intensity-based segmentation performs the best in segmenting the lesion region and produces more accurate findings.

Keywords: Skin cancer, image segmentation, intensity-based segmentation, skin lesions, dermoscopic image

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1. Bukhari M, Yasmin S, Habib A, Cheng X, Ullah F, Yoo J, Lee D. A novel framework for melanoma lesion segmentation using multiparallel depthwise separable and dilated convolutions with swish activations. J Healthc Eng. 2023; 2023: Article 1847115.
2. Labani S, Asthana S, Rathore K, Sardana K. Incidence of melanoma and nonmelanoma skin cancers in Indian and the global regions. J Cancer Res Ther. 2021; 17 (4): 906–911.
3. Maeda J, Kawano A, Yamauchi S, Suzuki Y, Marçal AR, Mendonça T. Perceptual image segmentation using fuzzy-based hierarchical algorithm and its application to dermoscopy images. In: 2008 IEEE Conference on Soft Computing in Industrial Applications, Muroran, Japan, June 25–27, 2008. pp. 66–71.
4. Oliveira RB, Mercedes Filho E, Ma Z, Papa JP, Pereira AS, Tavares JM. Computational methods for the image segmentation of pigmented skin lesions: a review. Computer Methods Programs Biomed. 2016; 131: 127–141.
5. Wong A, Scharcanski J, Fieguth P. Automatic skin lesion segmentation via iterative stochastic region merging. IEEE Trans Inform Technol Biomed. 2011; 15 (6): 929–936.
6. Yueksel ME, Borlu M. Accurate segmentation of dermoscopic images by image thresholding based on type-2 fuzzy logic. IEEE Trans Fuzzy Syst. 2009; 17 (4): 976–982.
7. Zhou H, Schaefer G, Celebi ME, Iyatomi H, Norton KA, Liu T, Lin F. Skin lesion segmentation using an improved snake model. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina, August 31–September 4, 2010. pp. 1974–1977.
8. Alphonse AS, Benifa JB, Muaad AY, Chola C, Heyat MB, Murshed BA, Abdel Samee N, Alabdulhafith M, Al-Antari MA. A hybrid stacked restricted Boltzmann machine with Sobel directional patterns for melanoma prediction in colored skin images. Diagnostics. 2023; 13 (6): 1104.
9. Joseph S, Olugbara OO. Preprocessing effects on performance of skin lesion saliency segmentation. Diagnostics. 2022; 12 (2): 344.
10. Kaur R, GholamHosseini H, Sinha R. Hairlines removal and low contrast enhancement of melanoma skin images using convolutional neural network with aggregation of contextual information. Biomed Signal Process Control. 2022; 76: 103653.
11. Sreedhar B, Manjunath Swamy BE, Kumar MS. A comparative study of melanoma skin cancer detection in traditional and current image processing techniques. In: 2020 Fourth International Conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC), Palladam, India, October 7–9, 2020. pp. 654–658.
12. Samraj J, Pavithra R. A comparative analysis on image enhancement and filtering techniques for efficient melanoma skin cancer detection. J Image Process Pattern Recogn Prog. 2022; 9 (2): 40–47.
13. Abbas Q, Fondón I, Rashid M. Unsupervised skin lesions border detection via two-dimensional image analysis. Computer Methods Programs Biomed. 2011; 104 (3): e1–e5.
14. Sarma TH, Sankar V, Shaik RA, editors. Emerging Trends in Electrical, Communications, and Information Technologies: Proceedings of ICECIT-2018. Singapore: Springer; 2018.
15. Xiang D, Wang W, Tang T, Guan D, Quan S, Liu T, Su Y. Adaptive statistical superpixel merging with edge penalty for PolSAR image segmentation. IEEE Trans Geosci Remote Sensing. 2019; 58 (4): 2412–2429.
16. Salih O, Viriri S, Adegun A. Skin lesion segmentation based on region-edge Markov random field. In: Bebis G, Boyle R, Parvin B, et al., editors. Proceedings of the International Symposium on Visual Computing. Cham, Switzerland: Springer International Publishing; 2019. pp. 407–418.
17. Jha D, Riegler MA, Johansen D, Halvorsen P, Johansen HD. DoubleU-Net: a deep convolutional neural network for medical image segmentation. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Rochester, MN, USA, July 28–30, 2020. pp. 558–564.
18. Dhar P, Guha S. Skin lesion detection using fuzzy approach and classification with CNN. Int J Eng Manuf. 2021; 11 (1): 11–18.
19. Javed R, Rahim MS, Saba T, Rashid M. Region-based active contour JSEG fusion technique for skin lesion segmentation from dermoscopic images. Biomed Res. 2019; 30 (6): 1–10.
20. El Khoukhi H, Filali Y, Yahyaouy A, Sabri MA, Aarab A. A hardware implementation of OTSU thresholding method for skin cancer image segmentation. In: 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), Fez, Morocco, April 3–4, 2019. pp. 1–5.
21. Russell G, Mcloughlin N, Nourrit V, Oakley JP. 2012 IEEE International Conference on Imaging Systems and Techniques (IST). In: IEEE International conference on Imaging Systems and Techniques, Manchester, UK, July 16–17, 2012. pp. 176–178.
22. Chan TF, Vese LA. Active contours without edges. IEEE Trans Image Process. 2001; 10 (2): 266–277.
23. Mumford DB, Shah J. Optimal approximations by piecewise smooth functions and associated variational problems. Commun Pure Appl Math. 1989; 42 (5): 577–685.
24. Osher S, Sethian JA. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J Comput Phys. 1988; 79 (1): 12–49.
25. Amer GM, Abushaala AM. Edge detection methods. In: 2015 2nd World Symposium on Web Applications and Networking (WSWAN), Sousse, Tunisia, March 21–23, 2015. pp. 1–7.
26. Xie FY, Qin SY, Jiang ZG, Meng RS. PDE-based unsupervised repair of hair-occluded information in dermoscopy images of melanoma. Computer Med Imaging Graphics. 2009; 33 (4): 275–282.
27. Castillejos H, Ponomaryov V, Nino-de-Rivera L, Golikov V. Wavelet transform fuzzy algorithms for dermoscopic image segmentation. Comput Math Methods Med. 2012; 2012: Article 578721.

Skin cancer remains among the deadliest forms of cancer, with an average survival rate ranging from 18% to 20%. Detecting and segmenting melanoma early is a challenging yet crucial undertaking. To effectively segment the lesions, several studies have presented automated and standard techniques. Likewise, traditional methods for segmentation frequently require inputs from humans and cannot be applied in automated systems. The aim of this article is to provide a comparative analysis of a various number of segmentation methods. The methods are explained along with their benefits and drawbacks. In numerous studies, effective methods for image segmentation of skin lesions have been successfully explored. Therefore, based on an analysis of the various segmentation approaches, intensity-based segmentation performs the best in segmenting the lesion region and produces more accurate findings.

Keywords: Skin cancer, image segmentation, intensity-based segmentation, skin lesions, dermoscopic image

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Bukhari M, Yasmin S, Habib A, Cheng X, Ullah F, Yoo J, Lee D. A novel framework for melanoma lesion segmentation using multiparallel depthwise separable and dilated convolutions with swish activations. J Healthc Eng. 2023; 2023: Article 1847115.
2. Labani S, Asthana S, Rathore K, Sardana K. Incidence of melanoma and nonmelanoma skin cancers in Indian and the global regions. J Cancer Res Ther. 2021; 17 (4): 906–911.
3. Maeda J, Kawano A, Yamauchi S, Suzuki Y, Marçal AR, Mendonça T. Perceptual image segmentation using fuzzy-based hierarchical algorithm and its application to dermoscopy images. In: 2008 IEEE Conference on Soft Computing in Industrial Applications, Muroran, Japan, June 25–27, 2008. pp. 66–71.
4. Oliveira RB, Mercedes Filho E, Ma Z, Papa JP, Pereira AS, Tavares JM. Computational methods for the image segmentation of pigmented skin lesions: a review. Computer Methods Programs Biomed. 2016; 131: 127–141.
5. Wong A, Scharcanski J, Fieguth P. Automatic skin lesion segmentation via iterative stochastic region merging. IEEE Trans Inform Technol Biomed. 2011; 15 (6): 929–936.
6. Yueksel ME, Borlu M. Accurate segmentation of dermoscopic images by image thresholding based on type-2 fuzzy logic. IEEE Trans Fuzzy Syst. 2009; 17 (4): 976–982.
7. Zhou H, Schaefer G, Celebi ME, Iyatomi H, Norton KA, Liu T, Lin F. Skin lesion segmentation using an improved snake model. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina, August 31–September 4, 2010. pp. 1974–1977.
8. Alphonse AS, Benifa JB, Muaad AY, Chola C, Heyat MB, Murshed BA, Abdel Samee N, Alabdulhafith M, Al-Antari MA. A hybrid stacked restricted Boltzmann machine with Sobel directional patterns for melanoma prediction in colored skin images. Diagnostics. 2023; 13 (6): 1104.
9. Joseph S, Olugbara OO. Preprocessing effects on performance of skin lesion saliency segmentation. Diagnostics. 2022; 12 (2): 344.
10. Kaur R, GholamHosseini H, Sinha R. Hairlines removal and low contrast enhancement of melanoma skin images using convolutional neural network with aggregation of contextual information. Biomed Signal Process Control. 2022; 76: 103653.
11. Sreedhar B, Manjunath Swamy BE, Kumar MS. A comparative study of melanoma skin cancer detection in traditional and current image processing techniques. In: 2020 Fourth International Conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC), Palladam, India, October 7–9, 2020. pp. 654–658.
12. Samraj J, Pavithra R. A comparative analysis on image enhancement and filtering techniques for efficient melanoma skin cancer detection. J Image Process Pattern Recogn Prog. 2022; 9 (2): 40–47.
13. Abbas Q, Fondón I, Rashid M. Unsupervised skin lesions border detection via two-dimensional image analysis. Computer Methods Programs Biomed. 2011; 104 (3): e1–e5.
14. Sarma TH, Sankar V, Shaik RA, editors. Emerging Trends in Electrical, Communications, and Information Technologies: Proceedings of ICECIT-2018. Singapore: Springer; 2018.
15. Xiang D, Wang W, Tang T, Guan D, Quan S, Liu T, Su Y. Adaptive statistical superpixel merging with edge penalty for PolSAR image segmentation. IEEE Trans Geosci Remote Sensing. 2019; 58 (4): 2412–2429.
16. Salih O, Viriri S, Adegun A. Skin lesion segmentation based on region-edge Markov random field. In: Bebis G, Boyle R, Parvin B, et al., editors. Proceedings of the International Symposium on Visual Computing. Cham, Switzerland: Springer International Publishing; 2019. pp. 407–418.
17. Jha D, Riegler MA, Johansen D, Halvorsen P, Johansen HD. DoubleU-Net: a deep convolutional neural network for medical image segmentation. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Rochester, MN, USA, July 28–30, 2020. pp. 558–564.
18. Dhar P, Guha S. Skin lesion detection using fuzzy approach and classification with CNN. Int J Eng Manuf. 2021; 11 (1): 11–18.
19. Javed R, Rahim MS, Saba T, Rashid M. Region-based active contour JSEG fusion technique for skin lesion segmentation from dermoscopic images. Biomed Res. 2019; 30 (6): 1–10.
20. El Khoukhi H, Filali Y, Yahyaouy A, Sabri MA, Aarab A. A hardware implementation of OTSU thresholding method for skin cancer image segmentation. In: 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), Fez, Morocco, April 3–4, 2019. pp. 1–5.
21. Russell G, Mcloughlin N, Nourrit V, Oakley JP. 2012 IEEE International Conference on Imaging Systems and Techniques (IST). In: IEEE International conference on Imaging Systems and Techniques, Manchester, UK, July 16–17, 2012. pp. 176–178.
22. Chan TF, Vese LA. Active contours without edges. IEEE Trans Image Process. 2001; 10 (2): 266–277.
23. Mumford DB, Shah J. Optimal approximations by piecewise smooth functions and associated variational problems. Commun Pure Appl Math. 1989; 42 (5): 577–685.
24. Osher S, Sethian JA. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J Comput Phys. 1988; 79 (1): 12–49.
25. Amer GM, Abushaala AM. Edge detection methods. In: 2015 2nd World Symposium on Web Applications and Networking (WSWAN), Sousse, Tunisia, March 21–23, 2015. pp. 1–7.
26. Xie FY, Qin SY, Jiang ZG, Meng RS. PDE-based unsupervised repair of hair-occluded information in dermoscopy images of melanoma. Computer Med Imaging Graphics. 2009; 33 (4): 275–282.
27. Castillejos H, Ponomaryov V, Nino-de-Rivera L, Golikov V. Wavelet transform fuzzy algorithms for dermoscopic image segmentation. Comput Math Methods Med. 2012; 2012: Article 578721.