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使用具有自适应各向同性搜索窗口的非局部均值算法对超声图像进行性能评估以改进唾液腺疾病检测的初步研究

Performance Evaluation of Ultrasound Images Using Non-Local Means Algorithm with Adaptive Isotropic Search Window for Improved Detection of Salivary Gland Diseases: A Pilot Study.

作者信息

Kim Ji-Youn

机构信息

Department of Dental Hygiene, Gachon University, 191, Hambakmoero, Yeonsu-gu, Incheon 21936, Republic of Korea.

出版信息

Diagnostics (Basel). 2024 Jul 4;14(13):1433. doi: 10.3390/diagnostics14131433.

DOI:10.3390/diagnostics14131433
PMID:39001323
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11241115/
Abstract

Speckle noise in ultrasound images (UIs) significantly reduces the accuracy of disease diagnosis. The aim of this study was to quantitatively evaluate its feasibility in salivary gland ultrasound imaging by modeling the adaptive non-local means (NLM) algorithm. UIs were obtained using an open-source device provided by SonoSkills and FUJIFILM Healthcare Europe. The adaptive NLM algorithm automates optimization by modeling the isotropic search window, eliminating the need for manual configuration in conventional NLM methods. The coefficient of variation (COV), contrast-to-noise ratio (CNR), and edge rise distance (ERD) were used as quantitative evaluation parameters. UIs of the salivary glands revealed evident visualization of the internal echo shape of the malignant tumor and calcification line using the adaptive NLM algorithm. Improved COV and CNR results (approximately 4.62 and 2.15 times, respectively) compared with noisy images were achieved. Additionally, when the adaptive NLM algorithm was applied to the UIs of patients with salivary gland sialolithiasis, the noisy images and ERD values were calculated almost similarly. In conclusion, this study demonstrated the applicability of the adaptive NLM algorithm in optimizing search window parameters for salivary gland UIs.

摘要

超声图像（UI）中的斑点噪声显著降低了疾病诊断的准确性。本研究的目的是通过对自适应非局部均值（NLM）算法进行建模，定量评估其在唾液腺超声成像中的可行性。使用SonoSkills和富士胶片欧洲医疗保健公司提供的开源设备获取超声图像。自适应NLM算法通过对各向同性搜索窗口进行建模来自动优化，消除了传统NLM方法中手动配置的需要。变异系数（COV）、对比度噪声比（CNR）和边缘上升距离（ERD）用作定量评估参数。使用自适应NLM算法，唾液腺的超声图像显示出恶性肿瘤内部回声形状和钙化线的明显可视化。与噪声图像相比，COV和CNR结果得到改善（分别约为4.62倍和2.15倍）。此外，当将自适应NLM算法应用于唾液腺涎石病患者的超声图像时，噪声图像和ERD值的计算结果几乎相似。总之，本研究证明了自适应NLM算法在优化唾液腺超声图像搜索窗口参数方面的适用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a99b/11241115/93436421904f/diagnostics-14-01433-g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a99b/11241115/6d25bca08679/diagnostics-14-01433-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a99b/11241115/1cf9578d47b5/diagnostics-14-01433-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a99b/11241115/0fdd01479da2/diagnostics-14-01433-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a99b/11241115/93436421904f/diagnostics-14-01433-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a99b/11241115/aab72d231038/diagnostics-14-01433-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a99b/11241115/11a987deeb86/diagnostics-14-01433-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a99b/11241115/8b1d41ed9629/diagnostics-14-01433-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a99b/11241115/d5a97fdaee3d/diagnostics-14-01433-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a99b/11241115/6d25bca08679/diagnostics-14-01433-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a99b/11241115/1cf9578d47b5/diagnostics-14-01433-g006.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a99b/11241115/93436421904f/diagnostics-14-01433-g008.jpg

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