Skin Lesion Segmentation Using K-means Clustering with Removal Unwanted Regions

The segmentation of skin lesions is crucial to the early and accurate identification of skin cancer by computerized systems. It is difficult to automatically divide skin lesions in dermoscopic images because of challenges such as hairs, gel bubbles, ruler marks, fuzzy boundaries and low contrast. We proposed an effective method based on K-means and trainable machine learning system to segment Region of Interest (ROI) in skin cancer images. The proposed method was implemented based into several stages including image conversion into grayscale, contrast image enhancement, removing artifacts with noise reduction, segmentation skin lesion from image using K-means clustering, segmenting ROI from unwanted objects based on a trainable machine learning system. The proposed model has been evaluated using ISIC 2017 publicly available dataset. The proposed method obtained a 90.09 accuracy outperforming several methods in the literature.

Anahtar Kelimeler:

Skin Cancer, Computer Aided Detection, Segmentation, Machine learning, K-means Clustering

Skin Lesion Segmentation Using K-means Clustering with Removal Unwanted Regions

The segmentation of skin lesions is crucial to the early and accurate identification of skin cancer by computerized systems. It is difficult to automatically divide skin lesions in dermoscopic images because of challenges such as hairs, gel bubbles, ruler marks, fuzzy boundaries, and low contrast. We proposed an effective method based on K-means and a trainable machine learning system to segment regions of interest (ROI) in skin cancer images. The proposed method was implemented in several stages, including grayscale image conversion, contrast image enhancement, artifact removal with noise reduction, skin lesion segmentation from image using K-means clustering, and ROI segmentation from unwanted objects using a trainable machine learning system. The proposed model has been evaluated using the ISIC 2017 publicly available dataset. The proposed method obtained a 90.09 accuracy rate, outperforming several methods in the literature.

Keywords:

Skin Cance, Computer Aided Detection, Segmentation, Machine learning, K-means Clustering,

PDF

___

Barata, C.; Celebi, M.E.; Marques, J.S. Explainable skin lesion diagnosis using taxonomies. Pattern Recognit. 2021, 110, 107413.
Liu, L.; Tsui, Y.Y.; Mandal, M. Skin lesion segmentation using deep learning with auxiliary task. J. Imaging 2021, 7, 67.
Mohapatra, S.; Abhishek, N.V.S.; Bardhan, D.; Ghosh, A.A.; Mohanty, S. Skin cancer classification using convolution neural networks. In Lecture Notes in Networks and Systems; Springer: Singapore, 2020; pp. 433–442.
Khan, M. A., Sharif, M., Akram, T., Damaševičius, R., & Maskeliūnas, R. (2021). Skin lesion segmentation and multiclass classification using deep learning features and improved moth flame optimization. Diagnostics, 11(5), 811.
Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer statistics, 2019. CA Cancer J. Clin. 2019, 69, 7–34.
Siegel, R.; Miller, K.; Jemal, A. Cancer statistics, 2018. CA Cancer J. Clin. 2017, 68, 7–30.
Sinz, C.; Tschandl, P.; Rosendahl, C.; Akay, B.N.; Argenziano, G.; Blum, A.; Braun, R.P.; Cabo, H.; Gourhant, J.-Y.; Kreusch, J.; et al. Accuracy of dermatoscopy for the diagnosis of nonpigmented cancers of the skin. J. Am. Acad. Dermatol. 2017, 77, 1100–1109.
Ünver, H. M., & Ayan, E. (2019). Skin lesion segmentation in dermoscopic images with combination of YOLO and grabcut algorithm. Diagnostics, 9(3), 72.
Wei, Z.; Song, H.; Chen, L.; Li, Q.; Han, G. Attention-based DenseUnet network with adversarial training for skin lesion segmentation. IEEE Access 2019, 7, 136616–136629.
Khan, M.A.; Akram, T.; Sharif, M.; Javed, K.; Rashid, M.; Bukhari, S.A.C. An integrated framework of skin lesion detection and recognition through saliency method and optimal deep neural network features selection. Neural Comput. Appl. 2019, 32, 15929–15948.
Khan, M.A.; Akram, T.; Sharif, M.; Shahzad, A.; Aurangzeb, K.; Alhussein, M.; Haider, S.I.; Altamrah, A. An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification. BMC Cancer 2018, 18.
Ali, A.R.; Couceiro, M.S.; Hassenian, A.E. Melanoma detection using fuzzy C-means clustering coupled with mathematical morphology. In Proceedings of the International Conference on Hybrid Intelligent Systems (HIS), Hawally, Kuwait, 14–16 December 2014; pp. 73–78.
Jaisakthi, S.M.; Mirunalini, P.; Aravindan, C. Automated skin lesion segmentation of Dermoscopic images using grabcut and kmeans algorithms. IET Comput. Vis. 2018, 12, 1088–1095.
Aljanabi, M.; Özok, Y.E.; Rahebi, J.; Abdullah, A.S. Skin lesion segmentation method for Dermoscopy images using artificial bee colony algorithm. Symmetry 2018, 10, 347.
Xu, Z., Sheykhahmad, F. R., Ghadimi, N., & Razmjooy, N. (2020). Computer-aided diagnosis of skin cancer based on soft computing techniques. Open Medicine, 15(1), 860-871.
Jana, E., Subban, R., & Saraswathi, S. (2017, December). Research on skin cancer cell detection using image processing. In 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)(pp. 1-8). IEEE.
Gautam, D., & Ahmed, M. (2015, December). Melanoma detection and classification using SVM based decision support system. In 2015 Annual IEEE India Conference (INDICON)(pp. 1-6). IEEE.
Agarwal, A., Issac, A., Dutta, M. K., Riha, K., & Uher, V. (2017, July). Automated skin lesion segmentation using K-means clustering from digital dermoscopic images. In 2017 40th International Conference on Telecommunications and Signal Processing (TSP) (pp. 743-748). IEEE