Statistical appearance models of mammographic masses

Michael Berks, Steven Caulkin, Rumana Rahim, Caroline Boggis, Susan Astley

    Research output: Chapter in Book/Report/Conference proceedingChapter


    We present a method for building generative statistical appearance models of mammographic masses. We address several key issues that limited the performance of previous methods. In particular, we use MDL optimization to generate more compact shape correspondences; we describe a technique for the accurate estimation of the background tissue on which a mass is superimposed; and we highlight the importance of choosing suitable weighting between shape, texture and scale components in the final combined model. Improvements in the ability of the model to characterize a set of 101 mammographic masses are quantified using leave-one-out testing, showing a reduction in mean square error per pixel from 3.109 using a previous method to 1.262 using the new appearance model. © 2008 Springer-Verlag Berlin Heidelberg.
    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
    Number of pages7
    Publication statusPublished - 2008
    Event9th International Workshop on Digital Mammography, IWDM 2008 - Tucson, AZ
    Duration: 1 Jul 2008 → …


    Conference9th International Workshop on Digital Mammography, IWDM 2008
    CityTucson, AZ
    Period1/07/08 → …


    • Appearance model
    • Breast cancer
    • Breast mass
    • Mammography
    • Shape analysis
    • Texture analysis
    • X-ray


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