Journal of Digital Imaging

, Volume 27, Issue 6, pp 833–847 | Cite as

Multi-Resolution Level Sets with Shape Priors: A Validation Report for 2D Segmentation of Prostate Gland in T2W MR Images

  • Fares S. Al-Qunaieer
  • Hamid R. Tizhoosh
  • Shahryar Rahnamayan
Article
First Online:

Abstract

The level set approach to segmentation of medical images has received considerable attention in recent years. Evolving an initial contour to converge to anatomical boundaries of an organ or tumor is a very appealing method, especially when it is based on a well-defined mathematical foundation. However, one drawback of such evolving method is its high computation time. It is desirable to design and implement algorithms that are not only accurate and robust but also fast in execution. Bresson et al. have proposed a variational model using both boundary and region information as well as shape priors. The latter can be a significant factor in medical image analysis. In this work, we combine the variational model of level set with a multi-resolution approach to accelerate the processing. The question is whether a multi-resolution context can make the segmentation faster without affecting the accuracy. As well, we investigate the question whether a premature convergence, which happens in a much shorter time, would reduce accuracy. We examine multiple semiautomated configurations to segment the prostate gland in T2W MR images. Comprehensive experimentation is conducted using a data set of a 100 patients (1,235 images) to verify the effectiveness of the multi-resolution level set with shape priors. The results show that the convergence speed can be increased by a factor of ≈ 2.5 without affecting the segmentation accuracy. Furthermore, a premature convergence approach drastically increases the segmentation speed by a factor of ≈ 17.9.

Keywords

Image segmentation MR imaging Image processing Prostate segmentation Multi-resolution Level set segmentation 
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Notes

Acknowledgments

The authors would like to thank King Abdulaziz City for Science and Technology for their scholarship and for their valuable support. This project was also partly supported by Natural Sciences and Engineering Research Council of Canada (NSERC). The authors would like to thank Segasist Technologies, Toronto, Canada, for providing the DICOM images of 100 patients and their manual segmentations. In addition, our thanks go to Bresson et al. for providing the code for level set image segmentation with shape-prior and to Dabov et al. for providing the code for non-local adaptive non-parametric filter used for noise removal.

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Copyright information

© Society for Imaging Informatics in Medicine 2014

Authors and Affiliations

  • Fares S. Al-Qunaieer
    • 1
  • Hamid R. Tizhoosh
    • 1
  • Shahryar Rahnamayan
    • 2
  1. 1.Systems Design EngineeringUniversity of WaterlooWaterlooCanada
  2. 2.Electrical and Computer EngineeringUniversity of Ontario Institute of Technology (UOIT)OshawaCanada

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