Performance analysis of various denoising filters on intravascular ultrasound coronary artery images

散斑噪声 人工智能 血管内超声 特征(语言学) 计算机科学 降噪 斑点图案 计算机视觉 模式识别(心理学) 曲线波变换 滤波器(信号处理) 双边滤波器 噪音(视频) 小波 中值滤波器 小波变换 医学 放射科 图像处理 图像(数学) 哲学 语言学
作者
Priyanka Arora,Parminder Singh,Akshay Girdhar,Rajesh Vijayvergiya
出处
期刊:International Journal of Imaging Systems and Technology [Wiley]
卷期号:33 (3): 965-984
标识
DOI:10.1002/ima.22833
摘要

Abstract Accurate diagnosis of atherosclerotic coronary artery stenosis largely depends on intravascular ultrasound (IVUS) image quality. Coherent sources present during IVUS acquisition generates speckle noise and obstructs the clear view of the artery. Denoising aims to remove speckle noise while preserving edges and other important features. Accordingly, speckle noise suppression serves as an important preprocessing step for IVUS image analysis. This paper presents a performance analysis of various despeckling filters such as Median, Wiener, Speckle Reducing Anisotropic Diffusion (SRAD) filter, Wavelet, Non‐local means (NLM) and curvelet based non‐local means (CNLM) for IVUS images. To evaluate the efficacy, each filter is evaluated for three categories of cross‐sectional IVUS images, that is, healthy, mild calcification, and dense calcification of real pullbacks from four patients. As it is challenging to denoise the IVUS images with complex lesions, that is, calcified arc >180°, in this performance analysis, the denoising performance for different denoising filters is considered not only for the healthy IVUS but also for complex lesions. Various measures such as peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), and expert evaluation are used to compare these approaches. The results depict that the CNLM denoising filter outperforms others in terms of PSNR and FSIM for speckle noise suppression for IVUS images while preserving the essential edge and feature details for better image visualization and diagnosis.
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