Computer-Aided Diagnosis of Liver Tumors Based on Multi-Image Texture Analysis of Contrast-Enhanced CT. Selection of the Most Appropriate Texture Features
Open Access
- 1 December 2013
- journal article
- Published by Walter de Gruyter GmbH in Studies in Logic, Grammar and Rhetoric
- Vol. 35 (1), 49-70
- https://doi.org/10.2478/slgr-2013-0039
Abstract
In this work, a system for the classification of liver dynamic contest- enhanced CT images is presented. The system simultaneously analyzes the images with the same slice location, corresponding to three typical acquisition moments (without contrast, arterial- and portal phase of contrast propagation). At first, the texture features are extracted separately for each acquisition mo- ment. Afterwards, they are united in one “multiphase” vector, characterizing a triplet of textures. The work focuses on finding the most appropriate features that characterize a multi-image texture. At the beginning, the features which are unstable and dependent on ROI size are eliminated. Then, a small subset of remaining features is selected in order to guarantee the best possible classification accuracy. In total, 9 extraction methods were used, and 61 features were calculated for each of three acquisition moments. 1511 texture triplets, corresponding to 4 hepatic tissue classes were recognized (hepatocellular carcinoma, cholangiocarcinoma, cirrhotic, and normal). As a classifier, an adaptive boosting algorithm with a C4.5 tree was used. Experiments show that a small set of 12 features is able to ensure classification accuracy exceeding 90%, while all of the 183 features provide an accuracy rate of 88.94%.Keywords
This publication has 15 references indexed in Scilit:
- The WEKA data mining softwareACM SIGKDD Explorations Newsletter, 2009
- Computer aided diagnosis based on medical image processing and artificial intelligence methodsNuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2006
- Automatic detection and classification of hypodense hepatic lesions on contrast-enhanced venous-phase CTMedical Physics, 2004
- 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
- Use of neural networks for feature based recognition of liver region on CT imagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- An automatic diagnostic system for CT liver image classificationIEEE Transactions on Biomedical Engineering, 1998
- A Decision-Theoretic Generalization of On-Line Learning and an Application to BoostingJournal of Computer and System Sciences, 1997
- Fractal feature analysis and classification in medical imagingIEEE Transactions on Medical Imaging, 1989
- Texture analysis using gray level run lengthsComputer Graphics and Image Processing, 1975
- Textural Features for Image ClassificationIEEE Transactions on Systems, Man, and Cybernetics, 1973