Performance Analysis of Magnetic Resonance Image Denoising Using Contourlet Transform

Author

Padmagireeshan, S.J. ; Johnson, R.C. ; Balakrishnan, Arun A. ; Paul, Varghese ; Pillai, A.V. ; Raheem, A. Abdul

Author_Institution

Dept. of AE&I, Rajagiri Sch. of Eng. & Technol., Kochi, India

fYear

2013

fDate

29-31 Aug. 2013

Firstpage

396

Lastpage

399

Abstract

A medical image denoising algorithm using contourlet transform is proposed and the performance of the proposed method is analysed with the existing methods. Noise in magnetic resonance imaging has a Rician distribution and unlike AWGN noise, Rician noise is signal dependent. Separating signal from Rician noise is a tedious task. The proposed approaches were compared with other transform methods such as wavelet thresholding and block DCT. Hard, soft and semi-soft thresholding techniques are described and applied to test images with threshold estimators like universal threshold. The results are compared based on the parameters: PSNR and MSE. Numerical results show that the contour let transform can obtained higher PSNR than wavelet based and block DCT based denoising algorithms.

Keywords

biomedical MRI; image denoising; medical image processing; source separation; transforms; AWGN noise; MSE; PSNR; Rician distribution; Rician noise; block DCT; contourlet transform; hard thresholding techniques; magnetic resonance image denoising; medical image denoising algorithm; semisoft thresholding techniques; signal separation; soft thresholding techniques; threshold estimators; universal threshold; wavelet thresholding; Discrete cosine transforms; Magnetic resonance imaging; Noise; Noise reduction; Wavelet analysis; Wavelet transforms; Magnetic Resonance Image; contourlet transform; denoising;

fLanguage

English

Publisher

ieee

Conference_Titel

Advances in Computing and Communications (ICACC), 2013 Third International Conference on

Conference_Location

Cochin

Type

conf

DOI

10.1109/ICACC.2013.84

Filename

6686416