Fuzzy C-Means Clustering with Histogram based Cluster Selection for Skin Lesion Segmentation using Non-Dermoscopic Images

TitleFuzzy C-Means Clustering with Histogram based Cluster Selection for Skin Lesion Segmentation using Non-Dermoscopic Images
Publication TypeJournal Article
Year of Publication2020
AuthorsDevi, S. S., N. H. Singh, and R. H. Laskar
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueSpecial Issue on Soft Computing
Volume6
Number1
Date Published03/2020
Pagination26-31
Abstract

Purpose – Pre-screening of skin lesion for malignancy is highly demanded as melanoma being a life-threatening skin cancer due to unpaired DNA damage. In this paper, lesion segmentation based on Fuzzy C-Means clustering using non-dermoscopic images has been proposed.
Design/methodology/approach – The proposed methodology consists of automatic cluster selection for FCM using the histogram property. The system used the local maxima along with Euclidean distance to detect the binomial distribution property of the image histogram, to segment the melanoma from normal skin. As the Value channel of HSV color image provides better and distinct histogram distribution based on the entropy, it has been used for segmentation purpose.
Findings – The proposed system can effectively segment the lesion region from the normal skin. The system provides a segmentation accuracy of 95.69 % and the comparative analysis has been performed with various segmentation methods. From the analysis, it has been observed that the proposed system can effectively segment the lesion region from normal skin automatically.
Originality/Value – This paper suggests a new approach for skin lesion segmentation based on FCM with automatic cluster selection. Here, different color channel has also been analyzed using entropy to select the better channel for segmentation. In future, the classification of melanoma from benign naevi can be performed.

KeywordsClustering, Fuzzy, Image Segmentation, Medical Images, Melanoma
DOI10.9781/ijimai.2020.01.001
URLhttps://www.ijimai.org/journal/sites/default/files/files/2020/01/ijimai20206_1_3_pdf_17885.pdf
AttachmentSize
IJIMAI20206_1_3.pdf1.2 MB