Mammographic segmentation and risk classification using a novel binary model based bayes classifier

Authors:
Wenda He;Erika R. E. Denton;Reyer Zwiggelaar
Affiliations:
Department of Computer Science, Aberystwyth University, Aberystwyth, UK;Department of Radiology, Norfolk & Norwich University Hospital, Norwich, UK;Department of Computer Science, Aberystwyth University, Aberystwyth, UK
Venue:
IWDM'12 Proceedings of the 11th international conference on Breast Imaging
Year:
2012

Citing 2
Cited 2

Parameter Estimation in Stochastic Mammogram Model by Heuristic Optimization Techniques

IEEE Transactions on Information Technology in Biomedicine
A Novel Breast Tissue Density Classification Methodology

IEEE Transactions on Information Technology in Biomedicine

Background intensity independent texture features for assessing breast cancer risk in screening mammograms

Pattern Recognition Letters
Texture and region dependent breast cancer risk assessment from screening mammograms

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

Clinical research has shown that the sensitivity of mammography is significantly reduced by increased breast density, which can mask some tumours due to dense fibroglandular tissue. In addition, there is a clear correlation between the overall breast density and mammographic risk. We present an automatic mammographic density segmentation approach using a novel binary model based Bayes classifier. The Mammographic Image Analysis Society (MIAS) database was used in a quantitative and qualitative evaluation. Visual assessment on the segmentation results indicated a good and consistent extraction of mammographic density. With respect to mammographic risk classification, substantial agreements were found between the classification results and ground truth provided by expert screening radiologists. Classification accuracies were 85% and 78% in Tabár and Breast Imaging Reporting and Data System (Birads) categories, respectively; whilst in the corresponding low and high categories, the classification accuracies were 93% and 88% for Tabár and Birads, respectively.