Abstract
This paper proposes a computer-aided cirrhosis diagnosis system to diagnose cirrhosis based on ultrasound images. We first propose a method to extract a liver capsule on an ultrasound image, then, based on the extracted liver capsule, we fine-tune a deep convolutional neural network (CNN) model to extract features from the image patches cropped around the liver capsules. Finally, a trained support vector machine (SVM) classifier is applied to classify the sample into normal or abnormal cases. Experimental results show that the proposed method can effectively extract the liver capsules and accurately classify the ultrasound images.
Highlights
Hepatic cirrhosis is a chronic, degenerative disease in which normal liver cells are damaged and are replaced by scar tissue [1]
Existing computer-aided cirrhosis diagnosis systems mainly focus on quantitatively analyzing the texture of parenchyma in liver ultrasound images by extracting texture features such as fractal features [7], statistical texture features [8,9,10], spectral features [11,12,13,14] or combined features [2,15,16,17,18]
We evaluate our method on a dataset consisting of 91 ultrasound images in which 44 images are from normal people and 47 images are from people with cirrhosis
Summary
Hepatic cirrhosis is a chronic, degenerative disease in which normal liver cells are damaged and are replaced by scar tissue [1]. It changes the structure of the liver and the blood vessels that nourish it. The disease reduces the ability of liver to synthesise proteins and produce hormones, nutrients, medications and poisons Chronic liver infection such as hepatitis B is the primary cause of cirrhosis. Computer-aided diagnosis systems can be used to diagnose cirrhosis at an early stage based on ultrasound images for timely treatments [2]. Ultrasound image liver capsule detection capsule guided classification results ( normal or diseased )
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