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Automatic segmentation of skin cancer images using adaptive color clustering

Ilea, Dana E. and Whelan, Paul F. (2006) Automatic segmentation of skin cancer images using adaptive color clustering. In: CIICT 2006 - China-Ireland International Conference on Information and Communications Technologies, 18-19 October 2006, Hangzhou, China.

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This paper presents the development of an adaptive image segmentation algorithm designed for the identification of the skin cancer and pigmented lesions in dermoscopy images. The key component of the developed algorithm is the Adaptive Spatial K-Means (A-SKM) clustering technique that is applied to extract the color features from skin cancer images. Adaptive-SKM is a novel technique that includes the primary features that describe the color smoothness and texture complexity in the process of pixel assignment. The A-SKM has been included in the development of a flexible color-texture image segmentation scheme and the experimental data indicates that the developed algorithm is able to produce accurate segmentation when applied to a large number of skin cancer (melanoma) images.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:image analysis; skin cancer; image segmentation; adaptive-SKM; diffusion-based filtering;
Subjects:Medical Sciences > Cancer
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > Research Institute for Networks and Communications Engineering (RINCE)
Official URL:
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:4660
Deposited On:03 Jul 2009 11:01 by DORAS Administrator. Last Modified 27 Oct 2017 09:58

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