Conditional Noise Filter for MRI Images with Revised Theory on Second-order Histograms

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Wai Ti Chan

Abstract

Previous research by the author has the theory that histograms of second-order derivatives are capable of determining differences between pixels in MRI images for the purpose of noise reduction without having to refer to ground truth. However, the methodology of the previous research resulted in significant false negatives in determining which pixel is affected by noise. The theory has been revised in this article through the introduction of an additional Laplace curve, leading to comparisons between the histogram profile and two curves instead of just one. The revised theory is that differences between the first curve and the histogram profile and the differences between the second curve and the profile can determine which pixels are to be selected for filtering in order to improve image clarity while minimizing blurring. The revised theory is tested with a modified average filter versus a generic average filter, with PSNR and SSIM for scoring. The results show that for most of the sample MRI images, the theory of pixel selection is more reliable at higher levels of noise but not as reliable at preventing blurring at low levels of noise.

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References

W.T. Chan, K.S. Sim & F.S. Abas, “Contrast Measurement with Histograms of Second-order Derivatives of Pixels for Magnetic Resonance Images,” Engineering Letters, vol. 27, no. 2, pp. 390-395, 2019.

W.T. Chan, K.S. Sim & F.S. Abas, “Pixel Filtering and Reallocation with Histograms of Second-order Derivatives of Pixel Values for Electron Microscope Images,” International Journal of Innovative Computing Information and Control, vol. 14, no. 3, pp. 915-928, 2018.

W.T. Chan & K.S. Sim, “Termination Factor for Iterative Noise Reduction in MRI Images Using Histograms of Second-order Derivatives,” International Journal of Computer Science, vol. 48, no. 1, pp. 174-180, 2021.

Y. Gao, Y.Y. Wang & J.H. Yu, “Optimized Resolution-Oriented Many-to-One Intensity Standardization Method for Magnetic Resonance Images,” Applied Sciences, vol. 9, no. 24, pp. 5531, 2019.

H.P. Singh, A. Nigam, A.K. Gautam, A. Bhardwaj & N. Singh, “Noise Reduction in Images using Enhanced Average Filter,” International Conference on Advances in Computer Engineering & Applications (ICACEA-2014), 2014.

R.C. Gonzalez & R.E. Woods, Digital Image Processing, 4th Edition, Pearson, 2018.

V. Kumar & P. Gupta, “Importance of Statistical Measures in Digital Image Processing,” International Journal of Emerging Technology and Advanced Engineering, vol. 2, no. 2, pp. 56-62, 2012.

M. Rottman, K. Maag, R. Chan, F. H¨uger, P. Schlicht & H. Gottschalk, “Detection of False Positive and False Negative Samples in Semantic Segmentation”, Computer Vision and Pattern Recognition, arXiv:1912.03673 [cs.CV