Abstract: In order to obtain the accurate information of atherothrombotic plaques in the cardiovasculars and assist the diagnosis and classification of the plaque tissues,this study applied apply a machine learning method to automatically characterize the atherothrombotic plaques in intravascular ultrasound(IVUS) grayscale images.In this study,207 plaque samples in the IVUS images were collected from 10 patients with cardiovascular disease in the hospital.Firstly,the size of a sliding patch is determined and then its centre pixel traverses in the plaque area.The values of the mean and entropy are calculated.Ten features of the patch along 4 directions are respectively obtained by using co-occurrence matrix method.Secondly,more texture features of the plaque region in the IVUS images are obtained by using Gabor filter and local binary pattern(LBP) methods.Finally,the classifiers of Liblinear,random forests and Harmonic to Minimum-Ge-neralized LVQ(H2M-GLVQ) are used to classify these pixels in the plaque tissues based on the features obtained through reducing dimension by using principal component analysis(PCA).The manual characterization by an experien-ced physician is considered as the gold standard.Results of the proposed automatic characterization method show the general identification rates of classifiers of random forests and H2M-GLVQ are over 80%.Compared with other two classifiers,the identification rate of random forests is relatively higher,i.e.89.04%,80.23% and 73.77% respectively for fibrotic,lipidic and calcified plaque tissues.

Key words: Intravascular ultrasound image,Automatic characterization,Texture features,Classification judgment