AUTOMATED TECHNIQUE FOR CAROTID PLAQUE CHARACTERIZATION AND CLASSIFICATION USING RDWT IN ULTRASOUND IMAGES
Asha Kulkarni, Shashidhar Sm · Journal of Cardiovascular Disease Research · 2021
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%.