A fuzzy segmentation method for Computed Tomography images
by Martin Tabakov
International Journal of Intelligent Information and Database Systems (IJIIDS), Vol. 1, No. 1, 2007

Abstract: This paper describes a way of medical image segmentation using an appropriately defined fuzzy clustering method based on a fuzzy similarity relation. The considered relation is defined in terms of the Minkowski metric. A fuzzy similarity relation–based image segmentation algorithm is also introduced. Next, a method for formal comparison between different data clusterings, based on a new distance model is proposed. To illustrate the obtained segmentation process some examples of computed tomography imaging are considered. For comparison purpose and also to access the clustering quality, corresponding results with using the classical fuzzy c–means algorithm are also presented.

Online publication date: Thu, 19-Apr-2007

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Information and Database Systems (IJIIDS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com