Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis
Joint Authors
Lui, Harvey
McLean, David I.
Wighton, Paul
Atkins, M. Stella
Mori, Greg
Lee, Tim K.
Source
International Journal of Biomedical Imaging
Issue
Vol. 2011, Issue 2011 (31 Dec. 2011), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2011-10-20
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wighton, Paul& Lee, Tim K.& Mori, Greg& Lui, Harvey& McLean, David I.& Atkins, M. Stella. 2011. Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis. International Journal of Biomedical Imaging،Vol. 2011, no. 2011, pp.1-10.
https://search.emarefa.net/detail/BIM-502881
Modern Language Association (MLA)
Wighton, Paul…[et al.]. Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis. International Journal of Biomedical Imaging No. 2011 (2011), pp.1-10.
https://search.emarefa.net/detail/BIM-502881
American Medical Association (AMA)
Wighton, Paul& Lee, Tim K.& Mori, Greg& Lui, Harvey& McLean, David I.& Atkins, M. Stella. Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis. International Journal of Biomedical Imaging. 2011. Vol. 2011, no. 2011, pp.1-10.
https://search.emarefa.net/detail/BIM-502881
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-502881