Rician Denoising Based on Correlated Local Features LMMSE Approach

Kinani, Jean Marie Vianney; Silva, Alberto Rosales; Mújica-Vargas, Dante; Gallegos-Funes, Francisco J.; Diaz, Eduardo Ramos

doi:10.1007/s10916-020-01696-2

Cited by 1 publication

(1 citation statement)

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“…Such methods may be classified into two groups. The first group refers to the methods, utilizing local information in the surrounding of a representative pixel, formed by a local window [22][23][24]. In this category, we can mention bilateral filters, average, median, weighted median filters (WMF), anisotropic diffusion (AD) and edge-avoiding wavelet (EAW) [25,26].…”

Section: Recent Workmentioning

confidence: 99%

Quantitative and Comparative Analysis of Effectivity and Robustness for Enhanced and Optimized Non-Local Mean Filter Combining Pixel and Patch Information on MR Images of Musculoskeletal System

Kubíček

Strycek

Černý

et al. 2021

Sensors

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In the area of musculoskeletal MR images analysis, the image denoising plays an important role in enhancing the spatial image area for further processing. Recent studies have shown that non-local means (NLM) methods appear to be more effective and robust when compared with conventional local statistical filters, including median or average filters, when Rician noise is presented. A significant limitation of NLM is the fact that thy have the tendency to suppress tiny objects, which may represent clinically important information. For this reason, we provide an extensive quantitative and objective analysis of a novel NLM algorithm, taking advantage of pixel and patch similarity information with the optimization procedure for optimal filter parameters selection to demonstrate a higher robustness and effectivity, when comparing with NLM and conventional local means methods, including average and median filters. We provide extensive testing on variable noise generators with dynamical noise intensity to objectively demonstrate the robustness of the method in a noisy environment, which simulates relevant, variable and real conditions. This work also objectively evaluates the potential and benefits of the application of NLM filters in contrast to conventional local-mean filters. The final part of the analysis is focused on the segmentation performance when an NLM filter is applied. This analysis demonstrates a better performance of tissue identification with the application of smoothing procedure under worsening image conditions.

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