Noise Removal of MRI Images with Different Similarities using Advance NLM Filtering
Abhishek Sharma1, Vijayshri Chaurasia2

1Abhishek Sharma, Electronics and Communication Department, MANIT, Bhopal, India.
2Dr. Vijayshri Chaurasia, Electronics and Communication Department, MANIT, Bhopal, India. 

Manuscript received on 04 August 2019. | Revised Manuscript received on 09 August 2019. | Manuscript published on 30 September 2019. | PP: 8476-8480 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6647098319/2019©BEIESP | DOI: 10.35940/ijrte.C6647.098319

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: In modern world, medical imaging has versatile application worldwide. It is very popular in the field of research and innovation. Medical image processing include the study of internal body structure like organs, tissues, etc., which provides much clear information of inner body structure using the digitalized data of human organs and help doctors to detect disease. Magnetic Resonance Imaging (MRI) is most effective and safe method for internal structure diagnosis. MRI images are generally magnitude images and they are follows by Rician distribution. In last few decades, many denoising techniques have been proposed like wavelet based techniques, Maximum likelihood (ML), bilateral filtering etc. But all those algorithms have some shortcomings and limitations. The purpose of our study is to propose a simple but effective advance Non-Local Means filtering approach for MRI denoising. Different distance matrix calculations have been introduced like Euclidean distance, Minkowski distance, Manhattan distance etc. These all are tested and identified best similarity measurement which preserves the image edges information and other information more effectively. The analysis is done on both the quality and quantity basis such as Peak Signal to Noise Ratio (PSNR) which shows the efficiency of noise removal technique and Mean Square Error (MSE) which represents the average of difference between pixels value of original image and denoised images.
Keywords: Magnetic Resonance Imaging, NLM Filter, PSNR, Rician Noise.

Scope of the Article:
Image Processing and Pattern Recognition