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COVID-19 CT图像的医学图像增强算法与质量评估指标的比较研究

A comparative study of medical image enhancement algorithms and quality assessment metrics on COVID-19 CT images.

作者信息

Mirza Muhammad Waqar, Siddiq Asif, Khan Ishtiaq Rasool

机构信息

Electrical Engineering Department, Pakistan Institute of Engineering and Technology, Multan, Pakistan.

College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia.

出版信息

Signal Image Video Process. 2023;17(4):915-924. doi: 10.1007/s11760-022-02214-2. Epub 2022 Apr 25.

Abstract

Medical imaging can help doctors in better diagnosis of several conditions. During the present COVID-19 pandemic, timely detection of novel coronavirus is crucial, which can help in curing the disease at an early stage. Image enhancement techniques can improve the visual appearance of COVID-19 CT scans and speed-up the process of diagnosis. In this study, we analyze some state-of-the-art image enhancement techniques for their suitability in enhancing the CT scans of COVID-19 patients. Six quantitative metrics, Entropy, SSIM, AMBE, PSNR, EME, and EMEE, are used to evaluate the enhanced images. Two experienced radiologists were involved in the study to evaluate the performance of the enhancement techniques and the quantitative metrics used to assess them.

摘要

医学成像有助于医生更好地诊断多种病症。在当前的新冠疫情大流行期间,及时检测新型冠状病毒至关重要,这有助于在疾病早期进行治愈。图像增强技术可以改善新冠CT扫描的视觉外观并加快诊断过程。在本研究中,我们分析了一些最先进的图像增强技术在增强新冠患者CT扫描方面的适用性。使用六种定量指标,即熵、结构相似性指数(SSIM)、平均边界误差(AMBE)、峰值信噪比(PSNR)、边缘均值误差(EME)和边缘均值熵误差(EMEE)来评估增强后的图像。两名经验丰富的放射科医生参与了该研究,以评估增强技术的性能以及用于评估它们的定量指标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39a2/9037579/4f3b5b5a1091/11760_2022_2214_Fig1_HTML.jpg

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