Mammographic Segmentation Based on Texture Modelling of Tabár Mammographic Building Blocks

Authors:
Wenda He;Izzati Muhimmah;Erika R. Denton;Reyer Zwiggelaar
Affiliations:
Department of Computer Science, Aberystwyth University, UK SY23 3DB;Department of Informatics, Universitas Islam Indonesia, Sleman, Indonesia 55584;Department of Radiology, Norfolk & Norwich University Hospital, Norwich, UK NR4 7UY;Department of Computer Science, Aberystwyth University, UK SY23 3DB
Venue:
IWDM '08 Proceedings of the 9th international workshop on Digital Mammography
Year:
2008

Citing 2
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Abstract

We present an approach to automate texton selection to achieve optimized mammogram segmentation results with respect to mammographic building blocks (i.e. nodular, linear, homogeneous, and radiolucent) as described by Tabár's tissue model. Such segmentation results are expected to lead to improvements in automatic mammographic risk assessment modelling. The texton selection process has three distinct components, covering a) texton ranking, b) outlier detection, and c) visual assessment. The initial results, on tissue specific regions and full mammographic images are promising, but at the same time indicate shortcomings, which are discussed.