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Automatic Prostate MR Image Segmentation with Sparse Label Propagation and Domain-Specific Manifold Regularization

  • Shu Liao
  • Yaozong Gao
  • Yinghuan Shi
  • Ambereen Yousuf
  • Ibrahim Karademir
  • Aytekin Oto
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)

Abstract

Automatic prostate segmentation in MR images plays an important role in prostate cancer diagnosis. However, there are two main challenges: (1) Large inter-subject prostate shape variations; (2) Inhomogeneous prostate appearance. To address these challenges, we propose a new hierarchical prostate MR segmentation method, with the main contributions lying in the following aspects: First, the most salient features are learnt from atlases based on a subclass discriminant analysis (SDA) method, which aims to find a discriminant feature subspace by simultaneously maximizing the inter-class distance and minimizing the intra-class variations. The projected features, instead of only voxel-wise intensity, will be served as anatomical signature of each voxel. Second, based on the projected features, a new multi-atlases sparse label fusion framework is proposed to estimate the prostate likelihood of each voxel in the target image from the coarse level. Third, a domain-specific semi-supervised manifold regularization method is proposed to incorporate the most reliable patient-specific information identified by the prostate likelihood map to refine the segmentation result from the fine level. Our method is evaluated on a T2 weighted prostate MR image dataset consisting of 66 patients and compared with two state-of-the-art segmentation methods. Experimental results show that our method consistently achieves the highest segmentation accuracies than other methods under comparison.

Keywords

Linear Discriminant Analysis Training Image Target Image Coarse Level Active Appearance Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shu Liao
    • 1
  • Yaozong Gao
    • 1
  • Yinghuan Shi
    • 1
  • Ambereen Yousuf
    • 2
  • Ibrahim Karademir
    • 2
  • Aytekin Oto
    • 2
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillUSA
  2. 2.Department of Radiology, Section of UrologyUniversity of ChicagoUSA

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