Cart (Loading....) | Create Account
Close category search window
 

Fully Automated Model-Based Prostate Boundary Segmentation Using Markov Random Field in Ultrasound Images

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
3 Author(s)
Vafaie, R. ; Dept. of Electr. & Comput. Eng., Ryerson Univ., Toronto, ON, Canada ; Alirezaie, J. ; Babyn, P.

In this paper, a new fully automated model-based approach for segmenting the prostate boundaries in transrectal ultrasound images is proposed. In the preprocessing step, the position of the initial model is automatically estimated using representative patterns. The Expectation Maximization algorithm (EM) and Markov Random Field (MRF) theory are utilized in the deformation strategy to optimally fit the initial model on the prostate boundaries. For the purpose of real time therapy, we propose a less computational complex EM approach for obtaining the probability distribution parameters. We also propose a new internal force energy that uses 2D geometric transformations for preventing the model fault deformation. Successful experimental results with the average Dice Similarity Coefficient (DSC) value 93.9% validate the algorithm.

Published in:

Digital Image Computing Techniques and Applications (DICTA), 2012 International Conference on

Date of Conference:

3-5 Dec. 2012

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.