Mammographic mass detection with statistical region merging

Mariusz Bajger, Fei Ma, Simon Williams, Murk Bottema

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    13 Citations (Scopus)

    Abstract

    An automatic method for detection of mammo-graphic masses is presented which utilizes statistical region merging for segmentation (SRM) and linear discriminant analysis (LDA) for classification. The performance of the scheme was evaluated on 36 images selected from the local database of mammograms and on 48 images taken from the Digital Database for Screening Mammography (DDSM). The Az value (area under the ROC curve) for classifying each region was 0.90 for the local dataset and 0.96 for the images from DDSM. Results indicate that SRM segmentation can form part of an robust and efficient basis for analysis of mammograms.

    Original languageEnglish
    Title of host publicationProceedings - 2010 Digital Image Computing
    Subtitle of host publication2010 Digital Image Computing: Techniques and Applications DICTA 2010
    Place of PublicationPiscataway, NJ
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages27-32
    Number of pages6
    ISBN (Print)978-0-7695-4271-3
    DOIs
    Publication statusPublished - 1 Dec 2010
    EventDigital Image Computing: Techniques and Applications 2010 - Sydney, Australia
    Duration: 1 Dec 20103 Dec 2010

    Publication series

    NameProceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010

    Conference

    ConferenceDigital Image Computing
    CountryAustralia
    CitySydney
    Period1/12/103/12/10

    Keywords

    • Mammography
    • Mass detection
    • Segmentation
    • Statistical region merging

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