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Advisor(s)
Abstract(s)
In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis, also known as, fatty liver, from ultrasound images. The features, automatically extracted from the ultrasound images used by the classifier, are basically
the ones used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The main novelty of the method is the utilization of the speckle noise that corrupts the ultrasound images to compute textural features of the liver parenchyma
relevant for the diagnosis. The algorithm uses the Bayesian framework to compute a noiseless image, containing anatomic and
echogenic information of the liver and a second image containing only the speckle noise used to compute the textural features.
The classification results, with the Bayes classifier using manually classified data as ground truth show that the automatic classifier reaches an accuracy of 95% and a 100% of sensitivity.
Description
Keywords
Radiology Ultrasound image Liver steatosis Clinical diagnosis
Citation
Ribeiro R, Seabra J, Sanches J. Fatty liver automatic diagnosis from ultrasound images. In Proceedings of RecPad 2008 - 14th Portuguese Conference on Pattern Recognition, Coimbra, October 31, 2008.