¹Department of Computer Science and Engineering, North China Institute of Aerospace Engineering, Langfang, China
²School of Computer Science and Technology, Harbin Institute of Technology, Harbin China

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Abstract—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.

Index Terms—Sparse representation, Image sequence, Texture feature, Thrombosis, Pectinate muscle

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Cite: Xiaofang Hou, Penghua Zhu, Yanxin Ma, "Classification of Cardiac Ultrasound Image Sequences Based on Sparse Representation," Journal of Software vol. 9, no. 9, pp. 2309-2314, 2014.