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Iterative Threshoding and Morphology Operation based Melanoma Image Segmentation

by Abbas Hussien Miry
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 118 - Number 2
Year of Publication: 2015
Authors: Abbas Hussien Miry
10.5120/20717-3060

Abbas Hussien Miry . Iterative Threshoding and Morphology Operation based Melanoma Image Segmentation. International Journal of Computer Applications. 118, 2 ( May 2015), 15-19. DOI=10.5120/20717-3060

@article{ 10.5120/20717-3060,
author = { Abbas Hussien Miry },
title = { Iterative Threshoding and Morphology Operation based Melanoma Image Segmentation },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 118 },
number = { 2 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 15-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume118/number2/20717-3060/ },
doi = { 10.5120/20717-3060 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:00:35.563241+05:30
%A Abbas Hussien Miry
%T Iterative Threshoding and Morphology Operation based Melanoma Image Segmentation
%J International Journal of Computer Applications
%@ 0975-8887
%V 118
%N 2
%P 15-19
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Dermoscopy is a suitable diagnostic technique for biology observation of pigmented skin lesions used in dermatology. Nowdays there is great interest in the prospects for methods of automatic image analysis for dermoscopy image, it Provide quantitative information about the lesion, which can be of link the doctor, and as a standalone early warning tool. This paper presents a good method of melanoma images segmentation. It based on threshoding as segmentation and mathematical morphology used to remove unwanted part in order to obtain a better segmentation. The proposed method is compared with the famous method of segmentation of skin lesions in images dermoscopic such adaptive thresholding and fuzzy K-means clustering for the segmentation and evaluated with two metrics, False Positive Rate (FPR) and the False Negtive Rate (FNR) , using the segmentation results obtained by a dermatologist experienced and ground truth.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Morphology Operation images segmentation skin lesions.

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