Ultrasonography (USG) is an excellent cost-effective modality in imaging of peripheral nerves. With the newer high-frequency probes with different footprints which allow high-resolution imaging at relatively superficial location, USG can detect and evaluate traumatic, inflammatory, infective, neoplastic, and compressive pathologies of the peripheral nerves. This article describes the technique for evaluation of nerves by USG as well as the USG appearances of normal and diseased peripheral nerves.
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.
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