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

Arshad, Javed and Wang, Yin Chai and Narayanan, Kulathuramaiyer and Muhammad Salim, Javed and Abdulhameed, Rakan Alenezi (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 1992-8645

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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.

Item Type: Article
Uncontrolled Keywords: Image Segmentation, Silhouette, Laplacian Pyramids (LP), Directional Filter Banks (DFB), Means, SNR (Signal To Noise Ratio), MSE (Mean Squared Error), unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak
Subjects: R Medicine > R Medicine (General)
T Technology > T Technology (General)
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Saman
Date Deposited: 07 Jun 2017 00:54
Last Modified: 07 Jun 2017 00:54
URI: http://ir.unimas.my/id/eprint/16526

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