Automated segmentation of brain MR images by combining Contourlet Transform and K-means Clustering techniques
Segmentation is usually conceived as a compulsory phase for the analysis and classification to the field of medical imaging. The aim of the paper is to find a means for the segmentation of brain from MR images by technique of combining Contourlet Transform and K-Means Clustering in an automatic way....
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Asian Research Publishing Network (ARPN)
2013
|
| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/16526/1/ARSHAD%20JAVED.pdf http://ir.unimas.my/id/eprint/16526/ http:///www.jatit.org/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Malaysia Sarawak |
| Language: | English |
| Summary: | Segmentation is usually conceived as a compulsory phase for the analysis and classification to the field of medical imaging. The aim of the paper is to find a means for the segmentation of brain from MR images by technique of combining Contourlet Transform and K-Means Clustering in an automatic way. De-noising is always an exigent problem in magnetic resonance imaging and significant for clinical diagnosis and computerized analysis such as tissue classification and segmentation. In this paper Contourlet transform has been used for noise removal and enhancement for the image superiority. The proposed technique is exclusively based upon the information enclosed within the image. There is no need for human interventions and extra information about the system. This technique has been tested on different types of MR images, and conclusion had been concluded. |
|---|