Int J Performability Eng ›› 2022, Vol. 18 ›› Issue (2): 108-116.doi: 10.23940/ijpe.22.02.p5.108116

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De-Speckling Techniques for T1 Weighted Brain MRI Images - A Statistical Comparison

Anjali Jaina,*, Navin Rajpala, and Rajesh Mehtab   

  1. aUniversity School of Information, Communication & Technology, Guru Gobind Singh Indraprastha University, New Delhi, 110078, India;
    bCSED, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
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Abstract: MRI is one of the most preferred imaging modality used by healthcare professionals for diagnosing brain tumors. Despite of aiding in diagnosis, the quality of MRI scans is degraded due to the distortion of visual signals, termed as ‘Speckle Noise'. Although other noises like Gaussian, Rician, Salt and pepper also degrade MRI images, speckle noise creates the most hindering effect as it diminishes the details of the image like edge details, contrast reduction, quality of fringe patterns, etc. As presence of noise in the medical images leads to ambiguity in the diagnosis being done, elimination of undesirable noise is an important pre-processing task. This paper evaluates different filters for their effectiveness in removing speckle noise and providing improved image quality perception for MRI scans. In this paper, an experimental dataset of T1 weighted brain MRI scans was used to evaluate the filters.

Key words: Speckle, MRI scans, de-speckling, image quality, de-noising, pre-processing