Conditional random fields and supervised learning in automated skin lesion diagnosis

Authors:
Paul Wighton;Tim K. Lee;Greg Mori;Harvey Lui;David I. McLean;M. Stella Atkins
Affiliations:
Dept. of Computing Science, Simon Fraser Univ., Canada and Dept. of Dermatology and Skin Science, Photomedicine Inst., Univ. of British Columbia and Vancouver Coastal Health Res. Inst., Canada and ...;Dept. of Computing Science, Simon Fraser Univ., Canada and Dept. of Dermatology and Skin Science, Photomedicine Inst., Univ. of British Columbia and Vancouver Coastal Health Res. Inst., Canada and ...;Department of Computing Science, Simon Fraser University, Burnaby, BC, Canada;Dept. of Dermatology and Skin Science, Photomedicine Institute, Univ. of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, Canada and Cancer Control Research Program ...;Department of Dermatology and Skin Science, Photomedicine Institute, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, Canada;Department of Computing Science, Simon Fraser University, Burnaby, BC, Canada and Department of Dermatology and Skin Science, Photomedicine Institute, University of British Columbia and Vancouver ...
Venue:
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
Year:
2011

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Abstract

Many subproblems in automated skin lesion diagnosis (ASLD) can be unified under a single generalization of assigning a label, from an predefined set, to each pixel in an image.We first formalize this generalization and then present two probabilistic models capable of solving it. The first model is based on independent pixel labeling using maximum a-posteriori (MAP) estimation. The second model is based on conditional random fields (CRFs), where dependencies between pixels are defined using a graph structure. Furthermore, we demonstrate how supervised learning and an appropriate training set can be used to automatically determine all model parameters. We evaluate both models' ability to segment a challenging dataset consisting of 116 images and compare our results to 5 previously published methods.