Abstract
The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP), a type of artificial neural network, to study the presence of disease conditions. An ultrasound (US) image shows echo-texture patterns, which defines the organ characteristics. Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. However, various ultrasound imaging artifacts and speckle noise make these echo-texture patterns difficult to identify and often hard to distinguish visually. Here, based on the extracted features from the ultrasonic images, we employed an artificial neural network for the diagnosis of disease conditions in liver and finding of the best classifier that distinguishes between abnormal and normal conditions of the liver. Comparison of the overall performance of all the feature classifiers concluded that “mixed feature set” is the best feature set. It showed an excellent rate of accuracy for the training data set. The gray level run length matrix (GLRLM) feature shows better results when the network was tested against unknown data.
Highlights
Ultrasound imaging modality is quite popular and most widely used modality for visualizing and studying the liver for any disease conditions without causing any pain or discomfort to the patient
The performance of the neural network was calculated by analysis of confusion matrix and the receiver operator characteristic curve (ROC)
The results showed that the selected mixed features yielded an accuracy of around 91.67% on the training set as compared to gray level run length matrix (GLRLM) features and gray-level co-occurrence matrix (GLCM) features, which yielded an accuracy of around 90% and 86.7%, respectively, on the training set
Summary
Ultrasound imaging modality is quite popular and most widely used modality for visualizing and studying the liver for any disease conditions without causing any pain or discomfort to the patient. Liver imaging is one of the best techniques of early detection of liver diseases and early detection is very important because it saves patients from further ailments such as enlarged stomach filled with ascites fluid, bleeding varices, and encephalopathy or sometimes jaundice. Liver disease conditions such as fatty liver, cirrhosis, and hepatomegaly are known for producing distinctive echo patterns during US imaging as shown in Figure 1; these images are known to be visually challenging for interpreting them because of their imaging artifacts and speckle noise.
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