Intra-Cardiac Tumor and Thrombi Classification in ECG based on Kernel Collaborative Representation

1R.S. Sidharth Raj and Dr.B. Karthik

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Abstract:

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.

Keywords:

Automatic Classification, Echocardiography, Intracardiac Tumor and Thrombi, Kernel Collaborative Representation.

Paper Details
Month7
Year2018
Volume22
IssueIssue 4
Pages53-62