Automated segmentation of skin lesions: Modified Fuzzy C mean thresholding based level set method

Masood, A; Al Jumaily, AA; Hoshyar, AN; Masood, O

Automated segmentation of skin lesions: Modified Fuzzy C mean thresholding based level set method

Masood, A Al Jumaily, AA

Hoshyar, AN Masood, O

Permalink

Publication Type:: Conference Proceeding
Citation:: 2013 16th International Multi Topic Conference, INMIC 2013, 2013, pp. 201 - 206
Issue Date:: 2013-01-01

Closed Access

	Filename	Description	Size
	2013004153OK.pdf		533.47 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Masood, A	en_US
dc.contributor.author	Al Jumaily, AA https://orcid.org/0000-0003-0297-2463	en_US
dc.contributor.author	Hoshyar, AN	en_US
dc.contributor.author	Masood, O	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	2013 16th International Multi Topic Conference, INMIC 2013, 2013, pp. 201 - 206	en_US
dc.identifier.isbn	9781479930432	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28418
dc.description.abstract	Accurate segmentation of skin lesion can play a vital role in early detection of skin cancer. Taking the complexity and varieties of skin lesion images into consideration, we propose a new algorithm that combines the advantages of clustering, thresholding and active contour methods currently being used independently for segmentation purposes. A modified Fuzzy C mean thresholding algorithm is applied to initialize level set automatically and also for estimating controlling parameters for level set evolution. The performance of level set segmentation is subject to appropriate initialization, so the proposed initialization method is compared to some other state of the art initialization methods present in literature. The work has been tested on a clinical database of 238 images. Parameters for performance evaluation are presented in detail. Increased true detection rate and reduced false positive and false negative errors confirm the effectiveness of the proposed method for skin cancer detection. © 2013 IEEE.	en_US
dc.relation.ispartof	2013 16th International Multi Topic Conference, INMIC 2013	en_US
dc.relation.isbasedon	10.1109/INMIC.2013.6731350	en_US
dc.title	Automated segmentation of skin lesions: Modified Fuzzy C mean thresholding based level set method	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical Engineering	en_US
dc.location.activity	Lahore	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Accurate segmentation of skin lesion can play a vital role in early detection of skin cancer. Taking the complexity and varieties of skin lesion images into consideration, we propose a new algorithm that combines the advantages of clustering, thresholding and active contour methods currently being used independently for segmentation purposes. A modified Fuzzy C mean thresholding algorithm is applied to initialize level set automatically and also for estimating controlling parameters for level set evolution. The performance of level set segmentation is subject to appropriate initialization, so the proposed initialization method is compared to some other state of the art initialization methods present in literature. The work has been tested on a clinical database of 238 images. Parameters for performance evaluation are presented in detail. Increased true detection rate and reduced false positive and false negative errors confirm the effectiveness of the proposed method for skin cancer detection. © 2013 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/28418