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A Noise-robust and Overshoot-free Alternative to Unsharp Masking for Enhancing the Acuity of MR Images

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Abstract

Poor acutance of images (unsharpness) is one of the major concerns in magnetic resonance imaging (MRI). MRI-based diagnosis and clinical interventions become difficult due to the vague textural information and weak morphological margins on images. A novel image sharpening algorithm named as maximum local variation-based unsharp masking (MLVUM) to address the issue of ‘unsharpness’ in MRI is proposed in this paper. In the MLVUM, the sharpened image is the algebraic sum of the input image and the product of the user-defined scale and the difference between the output of a newly designed nonlinear spatial filter named maximum local variation-controlled edge smoothing Gaussian filter (MLVESGF) and the input image, weighted by the normalised MLV. The MLVESGF is a locally adaptive 2D Gaussian edge smoothing kernel whose standard deviation is directly proportional to the local value of the normalized MLV. The values of the acutance-to-noise ratio (ANR) and absolute mean brightness error (AMBE) shown by the MLVUM on 100 MRI slices are 0.6463 ± 0.1852 and 0.3323 ± 0.2200, respectively. Compared to 17 state-of-the-art image sharpening algorithms, the MLVUM exhibited a higher ANR and lower AMBE. The MLVUM selectively enhances the sharpness of edges in the MR images without amplifying the background noise without altering the mean brightness level.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Goa, 403401, India

    Damodar Reddy Edla & V. R. Simi

  2. School of Bioengineering, VIT University, Bhopal, Madhya Pradesh, 466114, India

    Justin Joseph

Authors
  1. Damodar Reddy Edla
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  2. V. R. Simi
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  3. Justin Joseph
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Contributions

Dr. Damodar Reddy Edla (first author and supervisor): resources, data curation, supervision, and project administration. Simi V.R. (corresponding author): conceptualization, software, investigation, and writing (original draft). Dr. Justin Joseph (third author): methodology, validation, formal analysis.

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Correspondence to V. R. Simi.

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Edla, D.R., Simi, V.R. & Joseph, J. A Noise-robust and Overshoot-free Alternative to Unsharp Masking for Enhancing the Acuity of MR Images. J Digit Imaging 35, 1041–1060 (2022). https://doi.org/10.1007/s10278-022-00585-z

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