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    Multi-atlas based image selection with label image constraint

    Cao, Y. and Li, Xuelong and Yan, P. (2012) Multi-atlas based image selection with label image constraint. In: UNSPECIFIED (ed.) 11th International Conference on Machine Learning and Applications. Washington, USA: IEEE Computer Society, pp. 311-316. ISBN 9781467346511.

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    Atlas selection plays an important role in multiatlas based image segmentation. In atlas selection methods, manifold learning based techniques have recently emerged as very promisingly. However, due to the complexity of anatomical structures in raw images, it is difficult to get accurate atlas selection results by measuring only the distance between raw images on the manifolds. In this paper, we tackle this problem by proposing a label image constrained atlas selection (LICAS) method to exploit the shape and size information of the regions to be segmented from the label images. Constrained by the label images, a new manifold projection method is developed to help uncover the intrinsic similarity between the regions of interest across images. Compared with other existing methods, the experimental results of segmentation on 60 Magnetic Resonance (MR) images showed that the selected atlases are closer to the target structure and more accurate segmentation can be obtained by using the proposed method.


    Item Type: Book Section
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 06 Jun 2013 15:52
    Last Modified: 11 Oct 2016 15:27


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