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

Nechirvan Asaad Zebari; Emin Tenekeci

doi:10.54365/adyumbd.1112260

TR EN

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

Öz

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

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

References

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Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

Nechirvan Asaad Zebari This is me
Türkiye

Emin Tenekeci ^*
0000-0001-5944-4704
Türkiye

Publication Date

December 31, 2022

Submission Date

May 3, 2022

Acceptance Date

December 16, 2022

Published in Issue

Year 2022 Volume: 9 Number: 18

DOI

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

IZ

https://izlik.org/JA73LP76NC

APA

Zebari, N. A., & Tenekeci, E. (2022). Skin Lesion Segmentation Using K-means Clustering with Removal Unwanted Regions. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, 9(18), 519-529. https://doi.org/10.54365/adyumbd.1112260

AMA

1.Zebari NA, Tenekeci E. Skin Lesion Segmentation Using K-means Clustering with Removal Unwanted Regions. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2022;9(18):519-529. doi:10.54365/adyumbd.1112260

Chicago

Zebari, Nechirvan Asaad, and Emin Tenekeci. 2022. “Skin Lesion Segmentation Using K-Means Clustering With Removal Unwanted Regions”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 9 (18): 519-29. https://doi.org/10.54365/adyumbd.1112260.

EndNote

Zebari NA, Tenekeci E (December 1, 2022) Skin Lesion Segmentation Using K-means Clustering with Removal Unwanted Regions. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 9 18 519–529.

IEEE

[1]N. A. Zebari and E. Tenekeci, “Skin Lesion Segmentation Using K-means Clustering with Removal Unwanted Regions”, Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, vol. 9, no. 18, pp. 519–529, Dec. 2022, doi: 10.54365/adyumbd.1112260.

ISNAD

Zebari, Nechirvan Asaad - Tenekeci, Emin. “Skin Lesion Segmentation Using K-Means Clustering With Removal Unwanted Regions”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 9/18 (December 1, 2022): 519-529. https://doi.org/10.54365/adyumbd.1112260.

JAMA

1.Zebari NA, Tenekeci E. Skin Lesion Segmentation Using K-means Clustering with Removal Unwanted Regions. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2022;9:519–529.

MLA

Zebari, Nechirvan Asaad, and Emin Tenekeci. “Skin Lesion Segmentation Using K-Means Clustering With Removal Unwanted Regions”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, vol. 9, no. 18, Dec. 2022, pp. 519-2, doi:10.54365/adyumbd.1112260.

Vancouver

1.Nechirvan Asaad Zebari, Emin Tenekeci. Skin Lesion Segmentation Using K-means Clustering with Removal Unwanted Regions. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2022 Dec. 1;9(18):519-2. doi:10.54365/adyumbd.1112260

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