BIROn - Birkbeck Institutional Research Online

    Medical image segmentation using descriptive image features

    Yang, M. and Yuan, Y. and Li, Xuelong and Yan, P. (2011) Medical image segmentation using descriptive image features. In: Hoey, J. and McKenna, S. and Trucco, E. (eds.) Procedings of the British Machine Vision Conference. Manchester, UK: BMVA Press, 94.1-94.11. ISBN 978190172543X.

    Full text not available from this repository.

    Abstract

    Segmentation of medical images is an important component for diagnosis and treatment of diseases using medical imaging technologies. However, automated accurate medical image segmentation is still a challenge due to the difficulties in finding a robust feature descriptor to describe the object boundaries in medical images. In this paper, a new normal vector feature profile (NVFP) is proposed to describe the local image information of a contour point by concatenating a series of local region descriptors along the normal direction at that point. To avoid trapping by false boundaries caused by nonboundary image features, a modified scale invariant feature transform (SIFT) descriptor is developed. The number and locations of sample points for building NVFP are determined for each contour point, which are constrained by the neighboring anatomical structures and the statistical consistency of the training features. NVFP is incorporated into a model based method for image segmentation. The performance of our proposed method was demonstrated by segmenting prostate MR images. The segmentation results indicated that our method can achieve better performance compared with other existing methods.

    Metadata

    Item Type: Book Section
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 07 Jun 2013 09:34
    Last Modified: 11 Oct 2016 15:27
    URI: http://eprints.bbk.ac.uk/id/eprint/7366

    Statistics

    Downloads
    Activity Overview
    0Downloads
    115Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item