Evaluation of texture features in hepatic tissue characterization from non-enhanced CT images

Valavanis, Ioannis K; Mougiakakou, Stavroula G; Nikita, Alexandra; Nikita, Konstantina S (2007). Evaluation of texture features in hepatic tissue characterization from non-enhanced CT images. IEEE Engineering in Medicine and Biology Society conference proceedings, 2007, pp. 3741-3744. Piscataway, N.J.: IEEE Service Center 10.1109/IEMBS.2007.4353145

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Aim of this paper is to evaluate the diagnostic contribution of various types of texture features in discrimination of hepatic tissue in abdominal non-enhanced Computed Tomography (CT) images. Regions of Interest (ROIs) corresponding to the classes: normal liver, cyst, hemangioma, and hepatocellular carcinoma were drawn by an experienced radiologist. For each ROI, five distinct sets of texture features are extracted using First Order Statistics (FOS), Spatial Gray Level Dependence Matrix (SGLDM), Gray Level Difference Method (GLDM), Laws' Texture Energy Measures (TEM), and Fractal Dimension Measurements (FDM). In order to evaluate the ability of the texture features to discriminate the various types of hepatic tissue, each set of texture features, or its reduced version after genetic algorithm based feature selection, was fed to a feed-forward Neural Network (NN) classifier. For each NN, the area under Receiver Operating Characteristic (ROC) curves (Az) was calculated for all one-vs-all discriminations of hepatic tissue. Additionally, the total Az for the multi-class discrimination task was estimated. The results show that features derived from FOS perform better than other texture features (total Az: 0.802+/-0.083) in the discrimination of hepatic tissue.

Item Type:

Journal Article (Original Article)


04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Endocrinology, Diabetology and Clinical Nutrition

UniBE Contributor:

Mougiakakou, Stavroula




IEEE Service Center




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Date Deposited:

04 Oct 2013 15:23

Last Modified:

26 Jun 2016 01:44

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Web of Science ID:



https://boris.unibe.ch/id/eprint/37255 (FactScience: 207279)

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