Nowadays, computer vision as an interdisciplinary field is growing in different areas such as medical, electronics, etc. In the field, detection and particularly image segmentation is an essential task in which is difficult to find the appropriate one based on the application. In this paper, a new algorithm is proposed to segment the lesion from background. The algorithm is based on log edge detector with iterative median filtering. We have tested our algorithm on 20 dermoscopic images and compare the lesion detection results with those manually segmented by dermatologists. The experiments represent the effectiveness of proposed algorithm.
References
[1]
Dorrell, D.N. and Strowd, L.C. (2019) Skin Cancer Detection Technology. Dermatologic Clinics, 37, 527-536. https://doi.org/10.1016/j.det.2019.05.010
[2]
Di Leo, G., Liguori, C., Paolillo, A. and Sommella, P. (2008) An Improved Procedure for the Automatic Detection of Dermoscopy Structures in Digital ELM Images of Skin Lesions. In: Proceedings of the IEEE International Conference on Virtual Environment, Human-Computer Interfaces, and Measurement Systems, Istanbul, Turkey. https://doi.org/10.1109/VECIMS.2008.4592778
[3]
Shakourian Ghalejoogh, G., Montazery Kordy, H. and Ebrahimi, F. (2020) A Hierarchical Structure Based on Stacking Approach for Skin Lesion Classification. Expert Systems with Applications, 1451, Article No. 113127.
https://doi.org/10.1016/j.eswa.2019.113127
[4]
Garbe, C., Peris, K., Hauschild, A., Saiag, P., Middleton, M., Spatz, A., Grob, J.J., Malvehy, J., Bishop, J.N., Stratigos, A., Pehamberger, H. and Eggermont, A.M. (2012) Diagnosis and Treatment of Melanoma. European Consensus-Based Interdisciplinary Guideline. European Journal of Cancer, 48, 2375-2390.
https://doi.org/10.1016/j.ejca.2012.06.013
[5]
Xie, F. and Bovik, A. (2013) Automatic Segmentation of Dermoscopy Images Using Self-Generating Neural Networks Seeded by Genetic Algorithm. Pattern Recognition, 46, 1012-1019. https://doi.org/10.1016/j.patcog.2012.08.012
[6]
Pereira, P.M.M., Fonseca-Pinto, R., Pedro Paiv, R., Assuncao, P.A.A., Tavor, L.M.N., Thomaz, L.A. and Faria, S.M.M. (2020) Skin Lesion Classification Enhancement Using Border-Line Features-The Melanoma vs Nevus Problem. Biomedical Signal Processing and Control, 57, Article No. 101765.
https://doi.org/10.1016/j.bspc.2019.101765
[7]
Celebi, M.E., Iyatomi, H., Schaefer, G. and Stoecker, W.V. (2009) Lesion Border Detection in Dermoscopy Images. Computerized Medical Imaging and Graphics, 33, 148-153. https://doi.org/10.1016/j.compmedimag.2008.11.002
[8]
Beuren, A.T., Janasieivicz, R., Grando, N. and Facon, J. (2012) Skin Melanoma Segmentation by Morphological Approach. In: Proceedings of the International Conference on Advances in Computing, Communications and Informatics, ACM, New York. https://doi.org/10.1145/2345396.2345553
[9]
Wang, H., Moss, R.H., Chen, X., Stanley, R.J., Stoecker, W.V., Celebi, M.E., Malters, J.M., Grichnik, J.M., Marghoob, A.A., Rabinovitz, H.S., Menzies, S. and Szalapski, T.M. (2011) Modified Watershed Technique and Post-Processing for Segmentation of Skin Lesions in Dermoscopy Images. Computerized Medical Imaging and Graphics, 35, 116-120. https://doi.org/10.1016/j.compmedimag.2010.09.006
[10]
Zhou, H., Schaefer, G., Emre Celebi, M., Lin, F. and Liu, T. (2011) Gradient Vector Flow with Mean Shift for Skin Lesion Segmentation. Computerized Medical Imaging and Graphics, 35, 121-127. https://doi.org/10.1016/j.compmedimag.2010.08.002
[11]
Ren, T.B., Wang, H.H., Feng, H.L., Xu, C.S., Liu, G.S. and Ding, P. (2019) Study on the Improved Fuzzy Clustering Algorithm and Its Application in Brain Image Segmentation. Applied Soft Computing, 81, Article No. 105503.
https://doi.org/10.1016/j.asoc.2019.105503
[12]
Silveira, M., Nascimento, J.C., Marques, J.S., Marçal, A.R.S., Mendonça, T., Yamauchi, S., Maeda, J. and Rozeira, J. (2009) Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images. IEEE Journal of Selected Topics in Signal Processing, 3, 35-45. https://doi.org/10.1109/JSTSP.2008.2011119
[13]
Yuan, X., Situ, N. and Zouridakis, G. (2008) Automatic Segmentation of Skin Lesion Images Using Evolution Strategies. Biomedical Signal Processing and Control, 3, 220-228. https://doi.org/10.1016/j.bspc.2008.02.003
[14]
Xiao, L., Ouyang, H.L. and Fan, C.D. (2019) An Improved Otsu Method for Threshold Segmentation Based on Set Mapping and Trapezoid Region Intercept Histogram. Optik, 196, Article No. 163106.
https://doi.org/10.1016/j.ijleo.2019.163106
[15]
Ganster, H., Pinz, P., Rohrer, R., Wildling, E., Binder, M. and Kittler, H. (2001) Automated Melanoma Recognition. IEEE Transactions on Medical Imaging, 20, 233-239. https://doi.org/10.1109/42.918473
