By Topic

Automatic segmentation of intracranial hematoma and volume measurement

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
$31 $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

5 Author(s)

In this paper, a two-step segmentation method is developed for segmenting the hematoma area from brain CT images. The volume of hematoma area is calculated after the segmentation. During the second segmentation process, the method of two-dimensional entropy is introduced to separate hematoma. In using the method of two-dimensional entropy, most important is to find the optional threshold which can be achieved by an improved genetic algorithm (GA) i.e. hierarchical genetic algorithm (HGA). HGA is more efficient than simple GA in overcoming the shortcoming of standard GA in local optimal solution and low precision convergence. An experiment is designed to test the effectiveness of automatic segmentation. The results prove that the precision of automatic segmentation is better than artificial segmentation, and the clinical needs are met.

Published in:

Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE

Date of Conference:

20-25 Aug. 2008