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

• Published in: International journal of imaging systems and technology 2023-05, Vol.33 (3), p.965-984
• Main Authors: Arora, Priyanka, Singh, Parminder, Girdhar, Akshay, Vijayvergiya, Rajesh
• Format: Article
• Language: English
• Subjects: Atherosclerosis; Calcification; coronary artery stenosis; Coronary vessels; Diagnosis; Electromagnetic wave filters; Image acquisition; Image analysis; image denoising; Image quality; Intravascular Ultrasound; Lesions; Medical imaging; Noise generation; Noise reduction; Similarity; speckle noise; Ultrasonic imaging
• ISSN: 0899-9457; 1098-1098
• DOI: 10.1002/ima.22833

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.