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Applied Sciences
Volume 10
Issue 20
10.3390/app10207028
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Article
Peer-Review Record

Image Denoising Using Non-Local Means (NLM) Approach in Magnetic Resonance (MR) Imaging: A Systematic Review

Appl. Sci. 2020, 10(20), 7028; https://doi.org/10.3390/app10207028
by Yeong-Cheol Heo 1, Kyuseok Kim 2,* and Youngjin Lee 3,*
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Appl. Sci. 2020, 10(20), 7028; https://doi.org/10.3390/app10207028
Submission received: 9 September 2020 / Revised: 4 October 2020 / Accepted: 4 October 2020 / Published: 10 October 2020
(This article belongs to the Section Applied Physics General)

Round 1

Reviewer 1 Report

  1. page 3 line 111: please try to add references to the databases used in this work.
  2. please write either "MRI" or "MR imaging".
  3. page 3 figure 1: please add references to these 4 types.
  4. The crucial part of NLM is in equation 2. please try to further explain the equation of w(N_m, N_n)

Author Response

Thank you for review and comment in this manuscript.
We have revised the paper as your suggestion and responded point by point.
Please confirm attached revised manuscript and response files.

Best regards,

Youngjin Lee

Author Response File: Author Response.docx

Reviewer 2 Report

This paper reports a systematic literature search devoted to the non-local means (NLM) noise reduction algorithm applied to MRI images. The scientific papers published in three databases of publications that are focused on the noise reduction NLM algorithm are considered. Also, the review considered the types of NLM based noise reduction techniques for MRI images, the target organ for MRI (brain, breast, cardiac, and brain + knee, brain + breast, and brain + abdomen), if the studies were real or simulated experimental studies and the efficiency of the noise reduction algorithms.

Finally, the paper concludes on the research trends related to NLM noise reduction algorithm that was used mostly for brain MRI images. The effectiveness of NLM noise reduction algorithm was assessed using comparison evaluation parameter methods based on the similarities or dissimilarities inti a pair of images.

This paper is definitely publishable. I have a few minor requests: the authors are asked to conclude on the efficacy of the targeted NLM techniques (with fast computation and optimization term, with adaptive window or weight function, with wavelet transform and with statistical approaches).

Author Response

Thank you for review and comment in this manuscript.
We have revised the paper as your suggestion and responded point by point.
Please confirm attached revised manuscript and response files.

Best regards,

Youngjin Lee

Author Response File: Author Response.docx

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