A Comparison on Speckle Noise Suppression Algorithm for Intravascular Ultrasound (Ivus) Images

Abstract

Accurate and reliable diagnosis of carotid artery stenosis depends on the quality of IVUS images. Especially in ultrasound images where coherent sources are involved, speckle noise causes blurring and loss of information. Thus, methods to eliminate speckle noise plays an essential part in the field of medical imaging. This paper compares various speckle noise suppression algorithms for carotid artery ultrasound images. Speckle noise reduction algorithms that are implemented includes Homomorphic Wavelet Level 1 and Level 2, Perona-Malik (PM) filter, Modified PM1, Modified PM2, Adaptive PM, Butterworth Filter, Doubly Degenerative Diffusion (DDD), Speckle Reducing Anisotropic Diffusion (SRAD) and Total Variance (TV) filter. A quantitative evaluation is carried out by estimating Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Beta Metric and Natural Image Quality Evaluator (NIQE). The performance metrics shows that Homomorphic Wavelet Level1, Modified PM 2, Adaptive PM and SRAD are robust in eliminating speckle noise from carotid artery ultrasound images, thereby increasing its diagnostic accuracy. Though DDD and TV approach have good SNR and PSNR values, their low Beta metric and high NIQE values have made them ineffective.