摘要：To classify thrombosis and pectinate muscle in cardiac ultrasound image sequences, a classification method based on sparse representation is proposed. This method extracts GLCM based texture features to form the sample set and compute the sparse solution with coefficients how a test sample be represented by the training set. After that, two kinds of constraints and classification strategy are added to achieve the classification. Experiment results shows that the proposed approach can achieve a classification accuracy of 91.92%, significantly higher than other popular classifiers.
