BIROn - Birkbeck Institutional Research Online

    Segmenting images by combining selected atlases on manifold

    Cao, Y. and Yuan, Y. and Li, Xuelong and Turkbey, B. and Choyke, P.L. and Yan, P. (2011) Segmenting images by combining selected atlases on manifold. In: Fichtinger, G. and Martel, A. and Peters, T. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. Lecture Notes in Computer Science 6893. Berlin, Germany: Springer Verlag, pp. 272-279. ISBN 9783642236266.

    Full text not available from this repository.

    Abstract

    Atlas selection and combination are two critical factors affecting the performance of atlas-based segmentation methods. In the existing works, those tasks are completed in the original image space. However, the intrinsic similarity between the images may not be accurately reflected by the Euclidean distance in this high-dimensional space. Thus, the selected atlases may be away from the input image and the generated template by combining those atlases for segmentation can be misleading. In this paper, we propose to select and combine atlases by projecting the images onto a low-dimensional manifold. With this approach, atlases can be selected according to their intrinsic similarity to the patient image. A novel method is also proposed to compute the weights for more efficiently combining the selected atlases to achieve better segmentation performance. The experimental results demonstrated that our proposed method is robust and accurate, especially when the number of training samples becomes large.

    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 14:40
    Last Modified: 11 Oct 2016 15:27
    URI: http://eprints.bbk.ac.uk/id/eprint/7414

    Statistics

    Downloads
    Activity Overview
    0Downloads
    77Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item