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.
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He, W., Muhimmah, I., Denton, E.R.E., Zwiggelaar, R. (2008). Mammographic Segmentation Based on Texture Modelling of Tabár Mammographic Building Blocks. In: Krupinski, E.A. (eds) Digital Mammography. IWDM 2008. Lecture Notes in Computer Science, vol 5116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70538-3_3
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DOI: https://doi.org/10.1007/978-3-540-70538-3_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70537-6
Online ISBN: 978-3-540-70538-3
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