Comparison of Calcification Cluster Detection by CAD and Human Observers at Different Image Quality Levels

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

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

    Abstract

    Previous studies have compared the performance of human observers to the performance of human observers using CAD. Here we compare the performance of human observers to Hologic’s ImageChecker CAD system using a set of 162 images with simulated calcification clusters. The quality of the images was reduced to create four other image sets at different image qualities. These were analysed by the CAD system and the relevant information from the resulting DICOM structured reports was parsed. At the highest image quality level the figure of merit for the CAD was 0.82 and 0.84 for the humans. At the lowest image quality level the figure of merit for the CAD and humans were 0.62 and 0.55 respectively. At each image quality level there was no significant difference (p>0.05). The effect of changes in image quality on calcification detection was similar for human observers and the CAD system.
    Original languageEnglish
    Title of host publicationBreast Imaging: Lecture Notes in Computer Science 8539
    EditorsHiroshi Fujita, T Hara, C Muramatsu
    Place of PublicationSwitzerland
    PublisherSpringer Nature
    Pages643-649
    Number of pages7
    Publication statusPublished - Jun 2014
    EventInternational Workshop on Breast Imaging - Gifu, Japan
    Duration: 1 Jan 1824 → …

    Conference

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

    Keywords

    • mammography
    • CAD
    • detection
    • observer
    • image quality

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