Performance Analysis of Magnetic Resonance Image Denoising Using Contourlet Transform
- Resource Type
- Conference
- Authors
- Padmagireeshan, S.J.; Johnson, Renoh C.; Balakrishnan, Arun A.; Paul, Veena; Pillai, Ajith V.; Raheem, A. Abdul
- Source
- 2013 Third International Conference on Advances in Computing and Communications Advances in Computing and Communications (ICACC), 2013 Third International Conference on. :396-399 Aug, 2013
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Power, Energy and Industry Applications
Signal Processing and Analysis
Discrete cosine transforms
Noise reduction
Noise
Magnetic resonance imaging
Wavelet transforms
Wavelet analysis
Magnetic Resonance Image
denoising
contourlet transform
- Language
A medical image denoising algorithm using contour let 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.