Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis
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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2011-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2011/846312 |
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| Summary: | 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. |
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| ISSN: | 1687-4188 1687-4196 |