A novel hybrid system for skin lesion detection

Chiem, A; Al-Jumaily, A; Khushaba, RN

A novel hybrid system for skin lesion detection

Chiem, A Al-Jumaily, A

Khushaba, RN

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP, 2007, pp. 567 - 572
Issue Date:: 2007-12-01

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Field	Value	Language
dc.contributor.author	Chiem, A	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.contributor.author	Khushaba, RN https://orcid.org/0000-0001-8528-8979	en_US
dc.date.issued	2007-12-01	en_US
dc.identifier.citation	Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP, 2007, pp. 567 - 572	en_US
dc.identifier.isbn	1424415020	en_US
dc.identifier.isbn	9781424415021	en_US
dc.identifier.uri	http://hdl.handle.net/10453/3177
dc.description.abstract	In this paper, a new intelligent method of classifying benign and malignant melanoma lesions is implemented. The system consists of four stages; image pre-processing, image segmentation, feature extraction, and image classification. As the first step of the image analysis, pre-processing techniques are implemented to remove noise and undesired structures from the images using techniques such as median filtering and contrast enhancement. In the second step, a simple thresholding method is used to segment and localise the lesion, a boundary tracing algorithm is also implemented to validate the segmentation. Then, a wavelet approach is used to extract the features, more specifically Wavelet Packet Transform (WPT). Finally, the dimensionality of the selected features is reduced with Principal Component Cnalysis (PCA) and later supplied to an Artificial Neural Network and Support Vector Machine classifiers for classification. The ability to correctly discriminate between benign and malignant lesions was about 95% for the Artificial Neural Network and 85% for the Support Vector Machine classifier. © 2007 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP	en_US
dc.relation.isbasedon	10.1109/ISSNIP.2007.4496905	en_US
dc.title	A novel hybrid system for skin lesion detection	en_US
dc.type	Conference Proceeding
utslib.for	0902 Automotive Engineering	en_US
dc.location.activity	Melbourne, Australia	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/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In this paper, a new intelligent method of classifying benign and malignant melanoma lesions is implemented. The system consists of four stages; image pre-processing, image segmentation, feature extraction, and image classification. As the first step of the image analysis, pre-processing techniques are implemented to remove noise and undesired structures from the images using techniques such as median filtering and contrast enhancement. In the second step, a simple thresholding method is used to segment and localise the lesion, a boundary tracing algorithm is also implemented to validate the segmentation. Then, a wavelet approach is used to extract the features, more specifically Wavelet Packet Transform (WPT). Finally, the dimensionality of the selected features is reduced with Principal Component Cnalysis (PCA) and later supplied to an Artificial Neural Network and Support Vector Machine classifiers for classification. The ability to correctly discriminate between benign and malignant lesions was about 95% for the Artificial Neural Network and 85% for the Support Vector Machine classifier. © 2007 IEEE.

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http://hdl.handle.net/10453/3177