Image Retrieval and Classification of Carotid Plaque Ultrasound Images

Abstract

The extraction of multiple features from high-resolution ultrasound images of atherosclerotic carotid plaques, characterizing the plaque morphology and structure can be used for the classification and retrieval of similar plaques and the identification of individuals with asymptomatic carotid stenosis at risk of stroke. The objective of this work was to de- velop an automated image retrieval and classification system for the retrieval of similar carotid plaque ultrasound images, which will assist the physician in making his diagnostic decision based on similar previous cases. The neural self- organizing map (SOM) and the statistical K-nearest neighbor (KNN) classifiers were used for the retrieval and the classi- fication of the carotid plaques into symptomatic or asymptomatic. Twenty different feature sets including texture, shape, morphological, histogram and correlogram features were extracted from the carotid plaque images and the classification results were further combined in order to improve the success rate. The results on a dataset of 274 carotid plaque ultra- sound images show that image retrieval and classification for carotid plaque image are feasible and that features like multi-region histogram or texture can be used successfully for the identification of cases with similar symptoms output.