Abstract
This chapter addresses the problem of liver cirrhosis classification via ultrasound imaging. For this classification problem, a liver semiautomatic contour segmentation algorithm to characterize the morphology and a textural feature extraction scheme for the characterization of liver parenchyma are proposed. Phase congruency is used to enhance liver contour and help medical doctor in the inspection of liver surface. The regularity of the enhanced liver contour is characterized from geometrical features that are used together with US textural features in the classification process. The classification of the proposed method is tested by using support vector machine, Bayesian, Parzen and k-nearest neighbor classifiers and their performance are compared. The Bayes classifier outperformed the compared classifiers, attaining an overall accuracy of 87.22 %, with a detection rate of 88.52 % and 86.11 % for the non-cirrhotic and cirrhotic class, respectively.
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Ribeiro, R., Marinho, R.T., Suri, J., Sanches, J.M. (2014). Ultrasound Liver Surface and Textural Characterization for the Detection of Liver Cirrhosis. In: El-Baz, A., Saba, L., Suri, J. (eds) Abdomen and Thoracic Imaging. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-8498-1_6
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DOI: https://doi.org/10.1007/978-1-4614-8498-1_6
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