This paper proposes an effective way to segment melanoma skin lesion in colour dermoscopic images, using an edge-based approach. The proposed method, different methods were combined to improve the segmentation performance. These methods are morphological operations, bilateral filter, spline, polynomial model and canny edge detector. Different methods were tested to select the best method that was produced the best outcome. These testing methods, bilateral filter provided the highest PSNR amongst other filters such as median filter, Gaussian and average filter. Two statistical models were implemented polynomial model and linear regression and selected the best performance as polynomial model. Four edge detectors were applied to detect the edge of skin lesion and select the best segmentation accuracy. Manual border selection was used as the benchmark to evaluation the accuracy of the automatic border. The proposed method was able to achieve a good average accuracy of 96.69% based on canny edge detector. Our dataset consists of (70) dermoscopic images that includes melanoma and nevus.
Bilateral Filter; Spline; Polynomial Model; Linear Regression Model; Edge Detection; Canny; Melanoma; Skin Lesion
Full TextPDF - Viewed/Downloaded: 6 Times
 Ahmad, M. B. and Choi, T. S. (1999). Local threshold and boolean function based edge detection. Consumer Electronics, IEEE Transactions on, 45(3), 674-679.
 Stoecker, W. V., Gupta, K., Stanley, R. J., Moss, R. H. and Shrestha, B. (2005). Detection of asymmetric blotches (asymmetric structureless areas) in dermoscopy images of malignant melanoma using relative color. Skin Research and Technology, 11(3), 179-184.
 Celebi, M. E., Kingravi, H. A., Aslandogan, Y. A. and Stoecker, W. V. (2006, March). Detection of blue-white veil areas in dermoscopy images using machine learning techniques. In Medical Imaging (pp. 61445T-61445T). International Society for Optics and Photonics.
 Tomasi, C. and Manduchi, R. (1998, January). Bilateral filtering for gray and color images. In Computer Vision, 1998. Sixth International Conference on (pp. 839-846). IEEE.
 Francis, J. J. and De Jager, G. (2003, November). The bilateral median filter. In Proceedings of the 14th Symposium of the Pattern Recognition Association of South Africa.(Pires & Barcelos, 2007)
 Hu, Q., He, X. and Zhou, J. (2004, June). Multi-scale edge detection with bilateral filtering in spiral architecture. In Proceedings of the Pan-Sydney area workshop on Visual information processing (pp. 29-32). Australian Computer Society, Inc.
 Pires, V. B. and Barcelos, C. A. Z. (2007, October). Edge detection of skin lesions using anisotropic diffusion. In Intelligent Systems Design and Applications, 2007. ISDA 2007. Seventh International Conference on (pp. 363-370). IEEE.
 Chiu, L. C. and Fuh, C. S. (2008). A Robust Denoising Filter with Adaptive Edge Preservation. In Advances in Multimedia Information Processing-PCM 2008 (pp. 923-926). Springer Berlin Heidelberg.
 Sameh Arif, A., Mansor, S. and Logeswaran, R. (2011, December). Combined bilateral and anisotropic-diffusion filters for medical image de-noising. InResearch and Development (SCOReD), 2011 IEEE Student Conference on (pp. 420-424). IEEE.
 Garnavi, R., Aldeen, M. and Bailey, J. (2012). Computer-aided Diagnosis of Melanoma Using Border and Wavelet-based Texture Analysis.
 Yasmin, J. J. and Sathik, M. M. Skin Lesion Segmentation Algorithms using Edge Detectors. International Journal of Computer Science, 9.
 Bhonsle, D., Chandra, V. and Sinha, G. R. (2012). Medical image denoising using bilateral filter. International Journal of Image, Graphics and Signal Processing (IJIGSP), 4(6), 36.
 Mahmoud, M. K. A., Al-Jumaily, A. and Takruri, M. (2011, December). The automatic identification of melanoma by wavelet and curvelet analysis: Study based on neural network classification. In Hybrid Intelligent Systems (HIS), 2011 11th International Conference on (pp. 680-685). IEEE.
 Liu, D., Xiong, Y, Pulli, K., and Shapiro, L.. (2011). Estimating image segmentation difficulty. Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition (MLDM'11), Petra Perner (Ed.). Springer-Verlag, Berlin, Heidelberg, 484-495.
 Nagi, Zomrawi Mohammed and EimanEisa Ahmed Elhaj, “The Effect of Polynomial Order on Georeferencing Remote Sensing Images”, International Journal of Engineering and Innovative Technology (IJEIT), vol. 2 no. 8, pp. 5-8, 2013.
 Hani, A. F. M., Prakasa, E., Fitriyah, H., Nugroho, H., Affandi, A. M. and Hussein, S. H. (2011). High order polynomial surface fitting for measuring roughness of psoriasis lesion. In Visual Informatics: Sustaining Research and Innovations (pp. 341-351). Springer Berlin Heidelberg.
 Almedeij, J. (2012). Modeling Pan Evaporation for Kuwait by Multiple Linear Regression. The Scientific World Journal, 2012.
 Celebi, M. E., Iyatomi, H., Schaefer, G. and Stoecker, W. V. (2009). Lesion border detection in dermoscopy images. Computerized Medical Imaging and Graphics, 33(2), 148-153.
 Garnavi, R. and Aldeen, M. (2011). Optimized Weighted Performance Index for Objective Evaluation of Border-Detection Methods in Dermoscopy Images.Information Technology in Biomedicine, IEEE Transactions on, 15(6), 908-917.
 Iyatomi, H., Oka, H., Saito, M., Miyake, A., Kimoto, M., Yamagami, J. and Tanaka, M. (2006). Quantitative assessment of tumour extraction from dermoscopy images and evaluation of computer-based extraction methods for an automatic melanoma diagnostic system. Melanoma Research, 16(2), 183-190.
 EmreCelebi, M., Alp Aslandogan, Y., Stoecker, W. V., Iyatomi, H., Oka, H. and Chen, X. (2007). Unsupervised border detection in dermoscopy images. Skin Research and Technology, 13(4), 454-462.
 Garnavi, R., Aldeen, M., Celebi, M. E., Bhuiyan, A., Dolianitis, C. and Varigos, G. (2010). Automatic segmentation of dermoscopy images using histogram thresholding on optimal color channels. International Journal of Medicine and Medical Sciences, 1(2), 126-134.
 Garnavi, R., Aldeen, M., Celebi, M. E., Varigos, G. and Finch, S. (2011). Border detection in dermoscopy images using hybrid thresholding on optimized color channels. Computerized Medical Imaging and Graphics, 35(2), 105-115.
 Viana, J. M. D. C. (2009). Classification of skin tumours through the analysis of unconstrained images.
 Abbas, A.A., Guo, X.N. and Tan, W.H., (2013 February). “An improved Automatic Segmentation Skin Lesion from Dermoscopic Images Using Optimal RGB Channel”, Proceedings of the 2nd International Conference on Computer Science & Computational Mathematics, Kaula Lumpur, Malaysia.
 Maini, R., & Aggarwal, H. (2009). Study and comparison of various image edge detection techniques. International Journal of Image Processing , 3(1), 1-11.
 Eubank, R. L. (1999). Nonparametric regression and spline smoothing. CRC press.
 Sadeghi, M., Razmara, M., Lee, T. K. and Atkins, M. S. (2011). A novel method for detection of pigment network in dermoscopic images using graphs.Computerized Medical Imaging and Graphics, 35(2), 137-143.
 Razmjooy, N., Mousavi, B. S., Soleymani, F. and Khotbesara, M. H. A computer-aided diagnosis system for malignant melanomas. Neural Computing and Applications, 1-13.
 Abbas, A.A., Tan, W.H. and Guo, X.N. (2012). Combined Optimal Wavelet filters with Segmentation of Dermoscopic Skin Lesions, Springer- LNAI, vol. 7458, 722-727.
 He, Y., and Xie, F. (2013). Automatic skin lesion segmentation based on texture analysis and supervised learning. In Computer Vision–ACCV 2012 (pp. 330-341). Springer Berlin Heidelberg.
 Abbas, A.A., Logeswaran, R., Guo, X.N. and Tan, W.H. (2013, October). Lesion Border Detection in Dermoscopy Images Using Bilateral Filter, Accepted in 2013, IEEE International Conference Signal Processing and Applications,IEEE.
 Heath, M., Sarkar, S., Sanocki, T., & Bowyer, K. (1996, June). Comparison of edge detectors: a methodology and initial study. In Computer Vision and Pattern Recognition, 1996. Proceedings CVPR'96, 1996 IEEE Computer Society Conference on ( 143-148). IEEE.
 Gao, W., Zhang, X., Yang, L., & Liu, H. (2010, July). An improved Sobel edge detection. In Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on ( 5), 67-71. IEEE.
 Ziou, D., & Tabbone, S. (1998). Edge detection techniques-an overview.Pattern recognition and image analysis c/c of raspoznavaniye obrazov i analiz izobrazhenii, 8, 537-559.
 Zhao, Y., Gui, W., & Chen, Z. (2006, June). Edge detection based on multi-structure elements morphology. In Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on (Vol. 2, pp. 9795-9798). IEEE.
Copyright © 2020 Alaa Ahmed Abbas Al-abayechi, Fadeheela Sabri Abu-Almash
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License