A Fully Automatic Random Walker Segmentation for Skin Lesions in a Supervised Setting

Authors:
Paul Wighton;Maryam Sadeghi;Tim K. Lee;M. Stella Atkins
Affiliations:
School of Computing Science, Simon Fraser University, Canada and Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Canada ...;School of Computing Science, Simon Fraser University, Canada and Cancer Control Research Program and Cancer Imaging Department, BC Cancer Research Centre, Canada;School of Computing Science, Simon Fraser University, Canada and Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Canada ...;School of Computing Science, Simon Fraser University, Canada
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Year:
2009

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Abstract

We present a method for automatically segmenting skin lesions by initializing the random walker algorithm with seed points whose properties, such as colour and texture, have been learnt via a training set. We leverage the speed and robustness of the random walker algorithm and augment it into a fully automatic method by using supervised statistical pattern recognition techniques. We validate our results by comparing the resulting segmentations to the manual segmentations of an expert over 120 cases, including 100 cases which are categorized as difficult (i.e.: low contrast, heavily occluded, etc.). We achieve an F-measure of 0.95 when segmenting easy cases, and an F-measure of 0.85 when segmenting difficult cases.