Automatic tuning of a graph-based image segmentation method for digital mammography applications

Hirotaka Susukida, Fei Ma, Mariusz Bajger

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

    8 Citations (Scopus)

    Abstract

    Mammogram segmentation tasks underpin a wide range of registration, temporal analysis and detection algorithms. Unfortunately, finding an accurate, robust and efficient segmentation still remains a challenging problem in mammography. A recent segmentation technique, based on minimum spanning trees (MST segmentation), is known to be robust to typical mammogram distortions and computationally efficient. This method captures both local and global image information but the balance requires choosing a parameter. So far no automatic procedure to estimate this parameter has been proposed and the value was determined experimentally. In this paper a segmentation evaluation criterion, based on a measure of image entropy, is used to automatically optimize the granularity of an MST-based segmentation. The method is tested on a set of 82 random images taken from a commonly used mammogram database. The results show a dramatic improvement in the accuracy of a MST segmentation tuned up using the entropy-based criterion.
    Original languageEnglish
    Title of host publication2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro
    Subtitle of host publicationProceedings
    Place of PublicationPiscataway, NJ
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages89-92
    Number of pages4
    ISBN (Print) 978-1-4244-2003-2
    DOIs
    Publication statusPublished - May 2008
    Event2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Paris Marriott Rive Gauche Hotel & Conference Center, Paris, France
    Duration: 14 May 200817 May 2008
    Conference number: 5th

    Conference

    Conference2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro
    Country/TerritoryFrance
    CityParis
    Period14/05/0817/05/08

    Fingerprint

    Dive into the research topics of 'Automatic tuning of a graph-based image segmentation method for digital mammography applications'. Together they form a unique fingerprint.

    Cite this