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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10248377" target="_blank" >RIV/61989100:27240/21:10248377 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/1424-8220/21/12/4161" target="_blank" >https://www.mdpi.com/1424-8220/21/12/4161</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s21124161" target="_blank" >10.3390/s21124161</a>

Result language

angličtina

Original language name

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

Original language description

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. (C) 2021 by the author. Licensee MDPI, Basel, Switzerland.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF17_049%2F0008441" target="_blank" >EF17_049/0008441: Innovative therapeutic methods of musculoskeletal system in accident surgery</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Sensors

ISSN

1424-8220

e-ISSN

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Volume of the periodical

21

Issue of the periodical within the volume

12

Country of publishing house

CH - SWITZERLAND

Number of pages

21

Pages from-to

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UT code for WoS article

000666734400001

EID of the result in the Scopus database

2-s2.0-85107930477