Abstract
Classification of liver masses is important to early diagnosis of patients. In this paper, a diagnostic system of liver disease classification based on contrast enhanced ultrasound (CEUS) imaging is proposed. In the proposed system, the dynamic CEUS videos of hepatic perfusion are firstly retrieved. Secondly, time intensity curves (TICs) are extracted from the dynamic CEUS videos using sparse non-negative matrix factorizations. Finally, deep learning is employed to classify benign and malignant focal liver lesions based on these TICs. Quantitative comparisons demonstrate that the proposed method outperforms the compared classification methods in accuracy, sensitivity and specificity.
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