A Comparative Study on Despeckling Techniques in Intravascular Ultrasound Images

Abstract

Intravascular ultrasound (IVUS) imaging is a diagnostic imaging technique for tomographic visualization of coronary arteries and studying atherosclerotic diseases. These medical images are generally corrupted by multiplicative speckle noise due to the interference of the signal with the backscattered echoes. Speckle noise is an inherent property of medical ultrasound imaging, and it generally tends to reduce the image quality; thus, removing noise from the original medical image is a challenging problem for diagnosis applications. Trying to reduce speckle assists experts for better understanding of some pathologies and diagnosis purposes. Recently, several techniques have been proposed for effective suppression of speckle noise in ultrasound B-Scan images. In this paper, we overview the denoising techniques in both spatial and transform domain, and propose a new despeckling method based on Shearlet transform and ant colony optimization. To quantify the performance improvements of the speckle noise reduction methods, various evaluation criteria in addition to the visual quality of the denoised images are used. Our results showed that in general Shearlet transform with three different types of threshold selection is faster and more efficient than other techniques. In the case of accurate estimation of the variance of noise, Shearlet transform based on ACO yields acceptable results in comparison with others methods. This technique can obtain a favorable signal-to-noise ratio and successfully improve the quality of images and preserve edges and curves as well.