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Breast Density Classification Using Local Quinary Patterns with Various Neighbourhood Topologies

Rampun, Andrik, Scotney, Bryan, Morrow, Philip, Wang, Hui and Winder, John (2018) Breast Density Classification Using Local Quinary Patterns with Various Neighbourhood Topologies. Journal of Imaging, 4 (14). pp. 1-23. [Journal article]

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URL: http://dx.doi.org/10.3390/jimaging4010014

DOI: 10.3390/jimaging4010014

Abstract

This paper presents an extension of work from our previous study by investigating the use of Local Quinary Patterns (LQP) for breast density classification in mammograms on various neighbourhood topologies. The LQP operators are used to capture the texture characteristics of the fibro-glandular disk region (FGDroi) instead of the whole breast area as the majority of current studies have done. We take a multiresolution and multi-orientation approach, investigate the effects of various neighbourhood topologies and select dominant patterns to maximise texture information. Subsequently, 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 produced competitive results up to 86.13% and 82.02% accuracy based on 322 and 206 mammograms taken from the Mammographic Image Analysis Society (MIAS) and InBreast datasets, which is comparable with the state-of-the-art in the literature.

Item Type:Journal article
Keywords:breast density classification; computer aided diagnosis; local quinary patterns; breast mammography
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
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 > Artificial Intelligence and Applications
Computer Science Research Institute > Information and Communication Engineering
ID Code:39776
Deposited By: Professor Philip Morrow
Deposited On:20 Mar 2018 16:02
Last Modified:20 Mar 2018 16:02

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