A comprehensive framework for classification of brain tumour images using SVM and curvelet transform
by R. Karthik; R. Menaka; C. Chellamuthu
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 17, No. 2, 2015

Abstract: This work introduces an efficient approach for brain tumour detection using curvelet transform-based statistical features combined with GLCM (Grey Level Cooccurrence Matrix) texture features. The detection of the brain tumour is considered as a challenging problem, due to the irregularity of the highly varying structure of the tumour cells. The major contribution of the proposed work resides in the selection of significant features from both spatial and frequency domains for training the system. It combines the curvelet transform-based statistical features in the frequency domain with the GLCM texture features in the spatial domain. The proposed method applies skull-stripping as the pre-processing step to extract the brain portion from the MRI slice. This pre-processed image is subjected to watershed transform-based segmentation process to extract the necessary region of interest. From the extracted region of interest, frequency and spatial domain-based features are extracted. Finally, the classification model is developed using support vector machine. Experiments reveal that the proposed classifier is good in terms of accuracy.

Online publication date: Sun, 15-Mar-2015

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