An Euclidean Norm Based Nonlinear Filterfor Noise Removal In MR Images

G.L.N. Murthy*, B. Anuradha**
* Associate Professor, Department of Electronics and Communication Engineering, LBRCE, Mylavaram, Andhra Pradesh, India.
** Professor, Department of Electronics and Communication Engineering, S.V. Univeristy, Tirupati, Andhra Pradesh, India.
Periodicity:January - March'2017
DOI : https://doi.org/10.26634/jip.4.1.13520

Abstract

Adjacency or neighbourhood plays a vital role while removing noise in many key areas of image processing. This is in view of the fact that once noise corrupts an image, there can be a deviation from original intensity distribution. A location where noise has its impact will clearly have a value which differs from surroundings. The presence of this noise due to various means will definitely affect the accuracy of many computer vision related applications, which is very much critical in medical related applications. In the current work, an algorithm is developed which can handle multiple types of noise with minimum degradation. Unlike conventional filters which directly operate on pixel values, the algorithm concentrates on minimizing the differences between pixels in the process of noise reduction. Peak Signal to Noise Ratio (PSNR) is used as a performance metric for verifying the efficiency of the algorithm.

Keywords

MR Imaging, Euclidean Norm, Pixel Differences, Blurring

How to Cite this Article?

Murthy, G. L. N., and Anuradha, B. (2017). An Euclidean Norm Based Nonlinear Filter for Noise Removal In MR Images. i-manager’s Journal on Image Processing, 4(1), 16-22. https://doi.org/10.26634/jip.4.1.13520

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