Automated segmentation of brain MR images by combining Contourlet Transform and K-means Clustering techniques

Javed, A. and Chai, W.Y. and Kulathuramaiyer, N. and Javed, M.S. and Alenezi, A.R. (2013) Automated segmentation of brain MR images by combining Contourlet Transform and K-means Clustering techniques. Journal of Theoretical and Applied Information Technology, 54 (1). pp. 82-91. ISSN 19928645

Full text not available from this repository.
Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

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. © 2005-2013 JATIT & LLS. All rights reserved.

Item Type: Article
Additional Information: cited By 3
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 15:52
Last Modified: 09 Nov 2023 15:52
URI: https://khub.utp.edu.my/scholars/id/eprint/3956

Actions (login required)

View Item
View Item