Statistical Wavelet based Adaptive Noise Filtering Technique for MRI Modality

C. Anjanappa*, H. S. Sheshadri**
* Assistant Professor, Department of Electronics and Communication Engineering, The National Institute of Engineering, Mysore, Karnataka, India.
** Professor, Department of Electronics and Communication Engineering, People's Education Society College of Engineering, Mandya, Karnataka, India.
Periodicity:December - February'2018


In this research work, wavelet domain method is designed to filter noise in medical images. This method adapts to
various types of noise, which is dependent on the user or medical expert. Here, a single parameter can be used to
balance the preservation of relevant details and the level of noise reduction. This method needs the subsequent
information of the related image details across the resolution scales to perform a preliminary coefficient classification.
The statistical distributions of the coefficients can be estimated by using preliminary coefficient classification that
characterize the valuable image features and noise levels. Wavelet domain indicator is used to achieve the conversion
to the image features and noise level. The experimental results demonstrated noise suppression in Magnet Resonance
(MR) and Ultra Sound (US) images and its performance is validated by using quantitative and qualitative methods.


Noise Reduction, Wavelets, High Angular Resolution Diffusion Imaging (HARDI) Data, Joint Detection and Estimation, Magnetic Resonance Imaging (MRI).

How to Cite this Article?

Anjanappa, C., and Sheshadri, H. S. (2018). Statistical Wavelet based Adaptive Noise Filtering Technique for MRI Modality. i-manager’s Journal on Pattern Recognition, 4(4), 21-31.


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