Segmentation of skin cancer images based on gradient vector flow (GVF) snake

Mahmoud, MKA; Al-Jumaily, A

Segmentation of skin cancer images based on gradient vector flow (GVF) snake

Mahmoud, MKA Al-Jumaily, A

Permalink

Publication Type:: Conference Proceeding
Citation:: 2011 IEEE International Conference on Mechatronics and Automation, ICMA 2011, 2011, pp. 216 - 220
Issue Date:: 2011-11-17

Closed Access

	Filename	Description	Size
	2011001341OK.pdf		940.31 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	Mahmoud, MKA	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2011-11-17	en_US
dc.identifier.citation	2011 IEEE International Conference on Mechatronics and Automation, ICMA 2011, 2011, pp. 216 - 220	en_US
dc.identifier.isbn	9781424481149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19116
dc.description.abstract	A gradient vector flow (GVF) snake is proposed in this paper for the segmentation of skin cancer images. In order to make the snake insensitive to noise and be able to remove the hairs, an Adaptive Filter (Wiener and Median filters) is proposed. After the noise and hairs are removed, GVF snake will be used to segment the skin cancer region. The GVF snake extends the single direction and allows it to still be able to track the boundary of the skin cancer even if there are other objects near the skin cancer region. We have proposed new operators to find better edge map in a restored grey scale image. Subjective method has been used by comparing the performance of the proposed gradient vector flow (GVF) snake with other recommended operators of first derivative like Sobel, Prewitt, Roberts and second derivative like Laplacian. The root mean square error and root mean square of signal to noise ratio have been used for objective evaluation. Finally, to validate the efficiency of the filtering schemes different algorithms are proposed and the simulation study has been carried out. Experiments performed on 8(eight) cancer images show the effectiveness of the proposed algorithm. © 2011 IEEE.	en_US
dc.relation.ispartof	2011 IEEE International Conference on Mechatronics and Automation, ICMA 2011	en_US
dc.relation.isbasedon	10.1109/ICMA.2011.5985659	en_US
dc.title	Segmentation of skin cancer images based on gradient vector flow (GVF) snake	en_US
dc.type	Conference Proceeding
utslib.for	080106 Image Processing	en_US
dc.location.activity	Beijing, China	en_US
dc.location.activity	Taipei, TAIWAN
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/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

A gradient vector flow (GVF) snake is proposed in this paper for the segmentation of skin cancer images. In order to make the snake insensitive to noise and be able to remove the hairs, an Adaptive Filter (Wiener and Median filters) is proposed. After the noise and hairs are removed, GVF snake will be used to segment the skin cancer region. The GVF snake extends the single direction and allows it to still be able to track the boundary of the skin cancer even if there are other objects near the skin cancer region. We have proposed new operators to find better edge map in a restored grey scale image. Subjective method has been used by comparing the performance of the proposed gradient vector flow (GVF) snake with other recommended operators of first derivative like Sobel, Prewitt, Roberts and second derivative like Laplacian. The root mean square error and root mean square of signal to noise ratio have been used for objective evaluation. Finally, to validate the efficiency of the filtering schemes different algorithms are proposed and the simulation study has been carried out. Experiments performed on 8(eight) cancer images show the effectiveness of the proposed algorithm. © 2011 IEEE.

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

http://hdl.handle.net/10453/19116