Application of statistical inference in medical images

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Image segmentation is the first step in a number of research fields such as image processing, computer vision and machine learning. Image segmentation can be roughly presented as the grouping of individual image pixels into (meaningful/useful) partition of regions or objects. Although a large number of algorithms and approaches have been proposed, automated image segmentation continues to be a tantalizing and challenging problem. In this paper, we look at image segmentation as an inference problem and describe the Statistical Region Merging technique (Nock et al; 2004). Applications such as CT and mammographic image segmentation will be used for illustration.
    Original languageEnglish
    Title of host publicationANZIAM 2014
    PublisherAustralian and New Zealand Industrial and Applied Mathematics
    Pages62
    Number of pages1
    ISBN (Electronic)978-0-473-27350-7
    ISBN (Print)978-0-473-27349-1
    Publication statusPublished - 2 Feb 2014
    EventAustralian and New Zealand Industrial and Applied Mathematics 2014 Conference - Rotorua, New Zealand
    Duration: 2 Feb 20146 Feb 2014

    Conference

    ConferenceAustralian and New Zealand Industrial and Applied Mathematics 2014 Conference
    Abbreviated titleANZIAM 2014
    CountryNew Zealand
    CityRotorua,
    Period2/02/146/02/14

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