Computer aided diagnosis method for steatosis rating in ultrasound images using random forests

In this paper we discuss the problem of computer aided evaluation of the severity of steatosis disease using ultrasound images, the aim of the study being to compare the automatic evaluation of liver steatosis using random forests (RF) and support vector machine (SVM) classifiers. Material and method: One hundred and twenty consecutive patients with steatosis or normal liver, assessed by ultrasound by the same expert, were enrolled. We graded steatosis in four stages and trained two classifiers to rate the severity of disease, based on a large set of labeled images and a large set of features, including several features obtained by robust estimation techniques. We compared RF and SVM classifiers. The classifiers were trained using cross-validation. There was 80% of data randomly selected for training and 20% for testing the classifier. This procedure was performed 20 times. The main measure of performance was the accuracy. Results: From all cases, 10 were rated as normal liver, 70 as having mild, 33 moderate, and 7 severe steatosis. Our best experts' ratings were used as ground truth data. RF outperformed the SVM classifier and confirmed the ability of this classifier to perform well without feature selection. In contrast, the performance of the SVM classifier was poor without feature selection and improved significantly after feature selection. Conclusion: The ability and accuracy of RF to classify well the steatosis severity, without feature selection, were superior as compared to SVM.