A binary level set method based on k-Means for contour tracking on skin cancer images

Hoshyar, AN; Al-Jumaily, AA; Aung, YM

A binary level set method based on k-Means for contour tracking on skin cancer images

Hoshyar, AN Al-Jumaily, AA

Aung, YM

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2014, 2014, pp. 89 - 95
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Hoshyar, AN	en_US
dc.contributor.author	Al-Jumaily, AA https://orcid.org/0000-0003-0297-2463	en_US
dc.contributor.author	Aung, YM	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2014, 2014, pp. 89 - 95	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122551
dc.description.abstract	A great challenge of research and development activities have recently highlighted in segmenting of the skin cancer images. This paper presents a novel algorithm to improve the segmentation results of level set algorithm with skin cancer images. The major contribution of presented algorithm is to simplify skin cancer images for the computer aided object analysis without loss of significant information and to decrease the required computational cost. The presented algorithm uses k-means clustering technique and explores primitive segmentation to get initial label estimation for level set algorithm. The proposed segmentation method provides better segmentation results as compared to standard level set segmentation technique and modified fuzzy cmeans clustering technique.	en_US
dc.relation.ispartof	Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2014	en_US
dc.relation.isbasedon	10.2316/P.2014.818-072	en_US
dc.title	A binary level set method based on k-Means for contour tracking on skin cancer images	en_US
dc.type	Conference Proceeding
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	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	open_access
pubs.publication-status	Published	en_US

Abstract:

A great challenge of research and development activities have recently highlighted in segmenting of the skin cancer images. This paper presents a novel algorithm to improve the segmentation results of level set algorithm with skin cancer images. The major contribution of presented algorithm is to simplify skin cancer images for the computer aided object analysis without loss of significant information and to decrease the required computational cost. The presented algorithm uses k-means clustering technique and explores primitive segmentation to get initial label estimation for level set algorithm. The proposed segmentation method provides better segmentation results as compared to standard level set segmentation technique and modified fuzzy cmeans clustering technique.

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http://hdl.handle.net/10453/122551