AUTOMATED TECHNIQUE FOR CAROTID PLAQUE CHARACTERIZATION AND CLASSIFICATION USING RDWT IN ULTRASOUND IMAGES

Abstract

In this paper we proposed a Rational-Dilation Wavelet Transform (RDWT) technique to characterize plaques
recorded from high-resolution ultrasound images and develop a Computer-Aided Diagnosis (CADx) model. The
image acquisition and preprocessing, feature extraction and ensemble classifiers are automated for the classification
of plaque. The transition bands are constructed by using the transition function. From the sub-bands mean, standard
deviation, skewness, Renyi entropy and energy these statistical features are extracted. Salp Swarm Algorithm (SSA)
is used for optical features, the fundamental inspiration is the swarming behavior of slaps when navigating and
foraging in oceans. K Nearest Neighbor (k-NN), Probabilistic Neural Network (PNN) and Support Vector Machine
(SVM) classifiers are used in the Plaque Classification these techniques are compared in the classifier comparison.
Experimental results show the accuracy, specificity and sensitivity of proposed method in terms of algorithm and
classifiers. The percentage of accuracy in our method is 93%, the percentage of sensitivity in our method is 90% and
the percentage of specificity in our ultrasound images. A texture feature analysis and classifiers for the automated
carotid method is given as 94%.