Synthesising malignant breast masses in normal mammograms

Michael Berks, Chris Taylor, Rumana Rahim, David Barbosa Da Silva, Caroline Boggis, Susan Astley

    Research output: Contribution to journalArticlepeer-review


    Using mammograms in which signs of breast cancer have been synthesised overcomes the problem of obtaining a sufficiently large volume of real data with known ground truth for training and test purposes. This paper describes a fully automated method for generating synthetic spiculated masses. Statistical methods are used to model the appearance and location of a training set of real masses and their effect on surrounding breast tissue. The models are then used to synthesise the appearance of a malignant mass in an otherwise normal mammogram. By virtue of using generative statistical models, the synthesis process can be fully automated. In an observer study in which 10 expert mammogram readers attempted to distinguish between synthetic masses generated by the method and real masses, we report an area Az = 0.70±0.09 under the receiver operating characteristic. © 2010 Springer-Verlag.
    Original languageEnglish
    Pages (from-to)505-512
    Number of pages7
    JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Publication statusPublished - 2010


    • breast cancer
    • breast mass
    • DT-CWT
    • lesion synthesis
    • Mammography
    • statistical models


    Dive into the research topics of 'Synthesising malignant breast masses in normal mammograms'. Together they form a unique fingerprint.

    Cite this