Detection of Spiculated Lesions in Digital Mammograms Using a Novel Image Analysis Technique

Susan Astley, Hiroshi Fujita (Editor), T Hara (Editor), C Muramatsu (Editor)

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


    We have applied novel computational image analysis algorithms todetect malignant masses in mammograms. Our analysis focuses on spiculatedlesions, which are particularly challenging for computer-aided detectionmethods. The algorithm uses the principle of locally-normalised correlationcoefficients to identify patterns of motifs representing a spiculated feature. Acombination of correlation maps indicating the maximum correlation of the motifat each position relative to the mammogram, and of the pattern of angles forwhich this maximum is observed, are used to locate spiculated lesions in a verifiedtest dataset. The test set of images has been annotated by an expert reader,and allows objective evaluation of computer-aided detection procedures. In ablind test using an automated procedure our method identified 54% of the lesionlocations in the set of test images. This initial blind testing and comparisonwith expert annotated images, representing a ground truth, indicates feasibilityfor our approach. Optimisation of the procedure is expected to yield improvedperformance.
    Original languageEnglish
    Title of host publicationBreast Imaging: Lecture Notes in Computer Science 8539
    EditorsHiroshi Fujita, T Hara, C Muramatsu
    Place of PublicationSwitzerland
    PublisherSpringer Nature
    Number of pages8
    Publication statusPublished - Jun 2014
    EventInternational Workshop on Breast Imaging - Gifu, Japan
    Duration: 1 Jan 1824 → …


    ConferenceInternational Workshop on Breast Imaging
    CityGifu, Japan
    Period1/01/24 → …


    • Mammography
    • Detection
    • Cancer
    • Correlation
    • Image Analysis


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