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This paper proposes a novel fully automatic diagnosis method for liver cirrhosis based on the reading of high-frequency ultrasound images. The proposed method determines the cirrhosis stage via a deep-learning neural network. First, we feed an ultrasound image into an autoencoder to generate the capsule-enhanced version of the image and binarize the enhanced image. Then, we employ a partition-clustering...
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...
This paper proposes a novel method to extract and describe liver capsule contour in high-frequency ultrasound image for early diagnosis of HBV cirrhosis. The proposed method combines vertical gradient optimization and minimum of deflection error to approximate the outline of liver capsule using a number of reference points determined through interactive selection. We also propose a Continuity of Capsule...
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