SPECIAL SECTION ON MODEL-BASED MEDICAL IMAGE PROCESSING AND ANALYSISProbabilistic Models

Robust and efficient image segmentation approaches using Markov random field models

[+] Author Affiliations
Nariman Majdi Nasab

Indiana University, School of Dentistry, 1121 West Michigan Street, Indianapolis, Indiana?46202

Purdue University, Biomedical Engineering Department, West Lafayette, Indiana?47907 E-mail: nmajdina@ecn.purdue.edu

Mostafa Analoui

Indiana University, School of Dentistry, 1121 West Michigan Street, Indianapolis, Indiana?46202

Pfizer Global Research & Development, MS 8260-2617, Eastern Point Road, Groton, Connecticut?06340

Edward J. Delp

Purdue University, Biomedical Engineering Department, West Lafayette, Indiana?47907

J. Electron. Imaging. 12(1), 50-58 (Jan 01, 2003). doi:10.1117/1.1525280
History: Received May 1, 2001; Revised Jul. 1, 2002; Accepted Sep. 3, 2002; Online January 29, 2003
Text Size: A A A

Modified implementations of simulated annealing (SA) for image segmentation are proposed and evaluated. The segmentation procedure is based on a Markov random field (MRF) model for describing regions within an image. SA offers an iterative approach for computing a set of labels with maximum a posteriori (MAP) probability. However, this approach is computationally expensive and lacks robustness in noisy environments. We propose a random cost function (RCF) for computing a posterior energy function in SA. The proposed modified SA (SA-RCF) method depicts more robust performance for image segmentation than standard SA at the same computational cost. Alternatively, we proposed a multi-resolution (MR) approach based on MRF, which offers robust segmentation for noisy images with significant reduction in the computational cost. Computational cost and segmentation accuracy of each algorithm were examined using a set of simulated head computerized tomography (CT) phantoms. © 2003 SPIE and IS&T.

© 2003 SPIE and IS&T

Citation

Nariman Majdi Nasab ; Mostafa Analoui and Edward J. Delp
"Robust and efficient image segmentation approaches using Markov random field models", J. Electron. Imaging. 12(1), 50-58 (Jan 01, 2003). ; http://dx.doi.org/10.1117/1.1525280


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.