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Breast Density Classification using Local Ternary Patterns in Mammograms

Rampun, Andrik, Morrow, Philip, Scotney, Bryan and Winder, John (2017) Breast Density Classification using Local Ternary Patterns in Mammograms. In: Image Analysis and Recognition 14th International Conference, ICIAR 2017, Montreal, QC, Canada, July 5–7, 2017, Montreal, Canada. Springer. Vol 10317 8 pp. [Conference contribution]

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URL: http://dx.doi.org/10.1007/978-3-319-59876-5

DOI: 10.1007/978-3-319-59876-5

Abstract

This paper presents a method for breast density classifica- tion. Local ternary pattern operators are employed to model the ap- pearance of the fibroglandular disk region instead of the whole breast region as the majority of current studies have done. The Support Vector Machine classifier is used to perform the classification and a stratified ten-fold cross-validation scheme is employed to evaluate the performance of the method. The proposed method achieved 82.33% accuracy which is comparable with some of the best methods in the literature based on the same dataset and evaluation scheme.

Item Type:Conference contribution (Paper)
Keywords:Mammography, breast density, local ternary patterns, classification
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Faculty of Life and Health Sciences
Faculty of Life and Health Sciences > School of Health Sciences
Research Institutes and Groups:Institute of Nursing and Health Research > Centre for Health and Rehabilitation Technologies
Institute of Nursing and Health Research
Computer Science Research Institute
Computer Science Research Institute > Information and Communication Engineering
ID Code:38385
Deposited By: Professor Philip Morrow
Deposited On:26 Jul 2017 12:47
Last Modified:02 Jun 2018 22:23

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