Medical Image Analysis
Volume 16, Issue 2 , Pages 374-385, February 2012

Multiscale 3D shape representation and segmentation with applications to hippocampal/caudate extraction from brain MRI

  • Yi Gao

      Affiliations

    • Schools of Electrical & Computer Engineering and Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0250, USA
    • Corresponding Author InformationCorresponding author. Tel.: +1 404 385 5062.
  • ,
  • Benjamin Corn

      Affiliations

    • Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, Tel-Aviv 64239, Israel
  • ,
  • Dan Schifter

      Affiliations

    • Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, Tel-Aviv 64239, Israel
  • ,
  • Allen Tannenbaum

      Affiliations

    • Schools of Electrical & Computer Engineering and Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0250, USA

Received 5 May 2011; received in revised form 18 October 2011; accepted 20 October 2011. published online 03 November 2011.

Highlights

► A multiscale shape representation scheme is proposed. ► A fully automatic multiscale shape-based segmentation framework is proposed. ► The multiscale shape representation can be used with other shape analysis and shape-based techniques.

Abstract 

Extracting structure of interest from medical images is an important yet tedious work. Due to the image quality, the shape knowledge is widely used for assisting and constraining the segmentation process. In many previous works, shape knowledge was incorporated by first constructing a shape space from training cases, and then constraining the segmentation process to be within the learned shape space. However, such an approach has certain limitations due to the number of variations, eigen-shapemodes, that can be captured in the learned shape space. Moreover, small scale shape variances are usually overwhelmed by those in the large scale, and therefore the local shape information is lost. In this work, we present a multiscale representation for shapes with arbitrary topology, and a fully automatic method to segment the target organ/tissue from medical images using such multiscale shape information and local image features. First, we handle the problem of lacking eigen-shapemodes by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances existing in the training shapes captured by the statistical learning step are also represented at various scales. Note that by doing so, one can greatly enrich the eigen-shapemodes as well as capture small scale shape changes. Furthermore, in order to make full use of the training information, not only the shape but also the grayscale training images are utilized in a multi-atlas initialization procedure. By combining such initialization with the multiscale shape knowledge, we perform segmentation tests for challenging medical data sets where the target objects have low contrast and sharp corner structures, and demonstrate the statistically significant improvement obtained by employing such multiscale representation, in representing shapes as well as the overall shape based segmentation tasks.

Keywords: Multiscale shape representation, Shape-based segmentation

To access this article, please choose from the options below

Login to an existing account or Register a new account.

  • Purchase this article for 31.50 USD (You must login/register to purchase this article)

    Online access for 24 hours. The PDF version can be downloaded as your permanent record.

  • Subscribe to this title

    Get unlimited online access to this article and all other articles in this title 24/7 for one year.

  • Claim access now

    For current subscribers with Society Membership or Account Number.

  • Visit SciVerse ScienceDirect to see if you have access via your institution.
 

PII: S1361-8415(11)00140-X

doi:10.1016/j.media.2011.10.002

Medical Image Analysis
Volume 16, Issue 2 , Pages 374-385, February 2012