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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2011-01-01
|
| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2011/846312 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832549876581269504 |
|---|---|
| author | Paul Wighton Tim K. Lee Greg Mori Harvey Lui David I. McLean M. Stella Atkins |
| author_facet | Paul Wighton Tim K. Lee Greg Mori Harvey Lui David I. McLean M. Stella Atkins |
| author_sort | Paul Wighton |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ad3325c2431947bf9372aa06730bb655 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-ad3325c2431947bf9372aa06730bb6552025-02-03T06:08:18ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962011-01-01201110.1155/2011/846312846312Conditional Random Fields and Supervised Learning in Automated Skin Lesion DiagnosisPaul Wighton0Tim K. Lee1Greg Mori2Harvey Lui3David I. McLean4M. Stella Atkins5Department of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, CanadaDepartment of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, CanadaDepartment of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, CanadaDepartment of Dermatology and Skin Science, Photomedicine Institute, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, V5Z 4E8, CanadaDepartment of Dermatology and Skin Science, Photomedicine Institute, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, V5Z 4E8, CanadaDepartment of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, CanadaMany 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.http://dx.doi.org/10.1155/2011/846312 |
| spellingShingle | Paul Wighton Tim K. Lee Greg Mori Harvey Lui David I. McLean M. Stella Atkins Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis International Journal of Biomedical Imaging |
| title | Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis |
| title_full | Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis |
| title_fullStr | Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis |
| title_full_unstemmed | Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis |
| title_short | Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis |
| title_sort | conditional random fields and supervised learning in automated skin lesion diagnosis |
| url | http://dx.doi.org/10.1155/2011/846312 |
| work_keys_str_mv | AT paulwighton conditionalrandomfieldsandsupervisedlearninginautomatedskinlesiondiagnosis AT timklee conditionalrandomfieldsandsupervisedlearninginautomatedskinlesiondiagnosis AT gregmori conditionalrandomfieldsandsupervisedlearninginautomatedskinlesiondiagnosis AT harveylui conditionalrandomfieldsandsupervisedlearninginautomatedskinlesiondiagnosis AT davidimclean conditionalrandomfieldsandsupervisedlearninginautomatedskinlesiondiagnosis AT mstellaatkins conditionalrandomfieldsandsupervisedlearninginautomatedskinlesiondiagnosis |