Intra-Cardiac Tumor and Thrombi Classification in ECG based on Kernel Collaborative Representation
1R.S. Sidharth Raj and Dr.B. Karthik
Identification of intracardiac masses in echocardiograms is one important task in cardiac disease diagnosis. To im-prove 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 kernel collaborative representation classifier to distinguish two masses. Ninety-seven clinical echocardiogram sequences are collected to assess the effectiveness.
Automatic Classification, Echocardiography, Intracardiac Tumor and Thrombi, Kernel Collaborative Representation.