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

Year 2022, Volume: 9 Issue: 18, 519 - 529, 31.12.2022

Nechirvan Asaad Zebari Emin Tenekeci

https://doi.org/10.54365/adyumbd.1112260

Cited By: 1

Abstract

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.

Keywords

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

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

Abstract

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

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