Learning to diagnose cirrhosis via combined liver capsule and parenchyma ultrasound image features

This paper proposes a novel cirrhosis diagnosis method using high-frequency ultrasound imaging that is able to not only diagnose cirrhosis, but also determine its stage. We propose combined features extracted from both liver capsule and parenchyma texture to avoid the bias caused by considering only one aspect. The liver capsule is localized using a multi-scale, multi-objective optimization method and indices are proposed to measure the smoothness and continuity of the capsule. The parenchyma texture is modeled with Gaussian mixture model (GMM), and the lesions in the parenchyma are detected by a scale-space defect detection algorithm. The degree of pathological changes of the liver is quantitatively evaluated by 7 features describing morphology of the capsule and lesions in the parenchyma. Then SVM classifiers are trained to classify the samples into different cirrhosis stages. Experiment results demonstrate the effectiveness of the proposed method, which outperforms other 4 state-of-the-art methods and the proposed method that solely uses capsule or parenchyma texture features.