Characterization of CT liver lesions based on texture features and a multiple neural network classification scheme
- 1 January 2003
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
In this paper, a Computer Aided Diagnosis (CAD) system for the characterization of hepatic tissue from Computed Tomography (CT) images is presented. Regions of Interest (ROI's) corresponding to normal liver, cyst, hemangioma, and hepatocellular carcinoma, are drawn by an experienced radiologist on abdominal non-enhanced CT images. For each ROI, five distinct sets of texture features are extracted using the following methods: first order statistics, spatial gray level dependence matrix, gray level difference method, Laws' texture energy measures, and fractal dimension measurements. If the dimensionality of a feature set is greater than a predefined threshold, feature selection based on a Genetic Algorithm (GA) is applied. Classification of the ROI is then carried out by a system of five neural networks (NNs), each using as input one of the above feature sets. The members of the NN system (primary classifiers) are 4-class NNs trained by the backpropagation algorithm with adaptive learning rate and momentum. The final decision of the CAD system is based on the application of a voting scheme across the outputs of the individual NNs. The multiple classification scheme using the five sets of texture features results in significantly enhanced performance, as compared to the classification performance of the individual primary classifiersKeywords
This publication has 12 references indexed in Scilit:
- A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifierIEEE Transactions on Information Technology in Biomedicine, 2003
- Multiple Neural Network Classification Scheme for Detection of Colonic Polyps in CT Colonography Data SetsAcademic Radiology, 2003
- An automatic diagnostic system for CT liver image classificationIEEE Transactions on Biomedical Engineering, 1998
- Application of artificial neural networks for the classification of liver lesions by image texture parametersJapanese Journal of Clinical Oncology, 1996
- Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound imagesIEEE Transactions on Medical Imaging, 1996
- Texture analysis of CT imagesIEEE Engineering in Medicine and Biology Magazine, 1995
- Texture features for classification of ultrasonic liver imagesIEEE Transactions on Medical Imaging, 1992
- Rapid Texture IdentificationPublished by SPIE-Intl Soc Optical Eng ,1980
- A Comparative Study of Texture Measures for Terrain ClassificationIEEE Transactions on Systems, Man, and Cybernetics, 1976
- Textural Features for Image ClassificationIEEE Transactions on Systems, Man, and Cybernetics, 1973