De-speckle filtering in ultrasound imaging of the carotid artery

Abstract

The main objective of this paper is to evaluate the classification performance of de-speckle filtering on ultrasound imaging of the carotid atherosclerotic plaque. The following procedure was investigated on 230 images (recorded from 115 symptomatic, and 115 asymptomatic subjects): (i) six different de-speckle filters were used based on first order and higher order local statistics, anisotropic diffusion, and geometric properties

(ii) nine different texture feature sets were extracted, and (iii) the k-nearest neighbor classifier was used to classify a plaque as symptomatic or asymptomatic. The de-speckle filters based on higher order statistics, anisotropic speckle diffusion, and geometric properties gave a slightly higher percentage of correct classifications score than the original images.