Mammographic Segmentation Based on Texture Modelling of Tabár Mammographic Building Blocks

W. He, R. Zwiggelaar, E. R. E. Denton, I. Muhimmah

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

7 Citations (SciVal)


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.
Original languageEnglish
Title of host publicationDigital Mammography
Subtitle of host publication9th International Workshop, IWDM 2008 Tucson, AZ, USA, July 20-23, 2008 Proceedings
Number of pages8
Publication statusPublished - 2008

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743


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