title: Automatic classification of intracardiac tumor and thrombi in echocardiography based on sparse representation.
abstract: Identification of intracardiac masses in echocardiograms is one important task in cardiac disease diagnosis. To improve diagnosis accuracy, a novel fully automatic classification method based on the sparse representation is proposed to distinguish intracardiac tumor and thrombi in echocardiography. First, a region of interest is cropped to define the mass area. Then, a unique globally denoising method is employed to remove the speckle and preserve the anatomical structure. Subsequently, the contour of the mass and its connected atrial wall are described by the K-singular value decomposition and a modified active contour model. Finally, the motion, the boundary as well as the texture features are processed by a sparse representation classifier to distinguish two masses. Ninety-seven clinical echocardiogram sequences are collected to assess the effectiveness. Compared with other state-of-the-art classifiers, our proposed method demonstrates the best performance by achieving an accuracy of 96.91%, a sensitivity of 100%, and a specificity of 93.02%. It explicates that our method is capable of classifying intracardiac tumors and thrombi in echocardiography, potentially to assist the cardiologists in the clinical practice.
authors.forename: Yi
authors.initials: Y
authors.lastname: Guo
authors.forename: Yuanyuan
authors.initials: Y
authors.lastname: Wang
authors.forename: Dehong
authors.initials: D
authors.lastname: Kong
authors.forename: Xianhong
authors.initials: X
authors.lastname: Shu
mesh_terms: D000465:Algorithms; D003328:Coronary Thrombosis / Q000000981:diagnostic imaging*; D004452:Echocardiography / Q000379:methods*; D006338:Heart Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity