Despeckle Filtering of Ultrasound Images

Abstract

It is well known that speckle is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. This necessitates the need for robust despeckling techniques for both routine clinical practice and tele-consultation. The recent advancements in ultrasound instrumentation and portable ultrasound devices necessitate the need of more robust despeckling techniques for enhanced ultrasound medical imaging for both routine clinical practice and tele-consultation. The objective of this chapter is to introduce the theoretical background of a number of despeckle filtering techniques and to carry out a comparative evaluation of despeckle filtering based on texture analysis, image quality evaluation metrics, and visual evaluation by medical experts, on ultrasound images of the carotid artery bifurcation. In this chapter, a total of ten despeckle filters are presented based on local statistics, median filtering, pixel homogeneity, geometric filtering, homomorphic filtering, anisotropic diffusion, nonlinear coherence diffusion, and wavelet filtering. Our results suggest that the first-order statistics filter DsFlsmv gave the best performance, followed by the geometric filter DsFgf4d and the homogeneous mask area filter DsFlsminsc. These filters improved the class separation between the asymptomatic and the symptomatic classes based on the statistics of the extracted texture features, gave only a marginal improvement in the classification success rate, and improved the visual assessment carried out by the two experts. Most importantly, a despeckle filtering and evaluation protocol is proposed based on texture analysis, image quality evaluation metrics, and visual evaluation by experts. In conclusion, the proper selection of a despeckle filter is very important in the enhancement of ultrasonic imaging of the carotid artery. Further work is needed to evaluate at a larger scale and in clinical practice the performance of the proposed despeckle filters in the automated segmentation, texture analysis, and classification of carotid ultrasound imaging.