Classification of linear structures in mammograms using random forests

    Research output: Contribution to journalArticlepeer-review


    Classification of linear structures, such as blood vessels, milk ducts, spiculations and fibrous tissue can be used to aid the automated detection and diagnosis of mammographic abnormalities. We use a combination of dual-tree complex wavelet coefficients and random forest classification to detect and classify different types of linear structure. Encouraging results are presented for synthetic linear structures added to real mammographic backgrounds, and spicules in real mammograms. For spicule/non-spicule classification in real mammograms we report an area Az = 0.764 under the receiver operating characteristic. © 2010 Springer-Verlag.
    Original languageEnglish
    Pages (from-to)153-160
    Number of pages7
    JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Publication statusPublished - 2010


    • classification
    • dual-tree complex wavelet
    • linear structures
    • mammography
    • random forests


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