Jawbone texture classification using contourlets
by T. Kalpalatha Reddy, N. Kumaravel
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 4, No. 4, 2010

Abstract: In this paper, a feasibility study of human jawbone CT data set classification using contourlet transform is presented. A number of first-order, second-order texture parameters and grey-level run-length parameters are derived from the sub-bands of the contourlet decomposition and are used for classification. The results shows that by using the SVM classifier the combination of the features from grey level and 1st order statistics achieved overall classification accuracy >0.7273. Features selected from the contourlet-based co-occurrence Matrix performed better with overall classification accuracy >0.8667. To increase the success rate, the classification is done using the combination of CSF and CCF which produced a mean success rate of 100%.

Online publication date: Thu, 30-Sep-2010

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 Biomedical Engineering and Technology (IJBET):
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