By Topic

Automatic Segmentation Methods for Various CT Images Using Morphology Operation and Statistical Technique

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.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Myung-Eun Lee ; Department of Computer Science, Chonnam National University, Korea, ; Soo-Hyung Kim ; Sun-Worl Kim ; Sung-Ryul Oh

In this paper, we present an automatic segmentation method for medical image based on the statistical technique. Here we use the morphological operations to determine automatically the number of clusters or objects composing a given image without any prior knowledge and adopt the Gaussian mixture model to mode an image statistically. Next, the deterministic annealing expectation maximization algorithm is employed to estimate the parameters of the GMM for the clustering algorithm. We apply the statistical technique for automatic segmentation of input CT image. The experimental results show that our method can segment exactly various CT images.

Published in:

2007 IEEE International Conference on Intelligent Computer Communication and Processing

Date of Conference:

6-8 Sept. 2007