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Authors: Daniel Smutek 1 ; Akinobu Shimizu 2 ; Ludvik Tesar 2 ; Hidefumi Kobatake 2 and Shigeru Nawano 3

Affiliations: 1 Tokyo University of Agriculture and Technology; 1st Medical Faculty, Charles University, Czech Republic ; 2 Tokyo University of Agriculture and Technology, Japan ; 3 National Cancer Center Hospital East, Japan

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Bayesian Networks ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Machine Perception: Vision, Speech, Other ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: An application of artificial intelligence in the field of automatization in medicine is described. A computer-aided diagnostic (CAD) system for focal liver lesions automatic classification in CT images is being developed. The texture analysis methods are used for the classification of hepatocellular cancer and liver cysts. CT contrast enhanced images of 20 adult subjects with hepatocellular carcinoma or with non-parasitic solitary liver cyst were used as entry data. A total number of 130 spatial and second-order probabilistic texture features were computed from the images. Ensemble of Bayes classifiers was used for the tissue classification. Classification success rate was as high as 100% when estimated by leave-one-out method. This high success rate was achieved with as few as one optimal descriptive feature representing the average deviation of horizontal curvature computed from original pixel gray levels. This promising result allows next amplification of this approach in distingu ishing more types of liver diseases from CT images and its further integration to PACS and hospital information systems. (More)

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Paper citation in several formats:
Smutek, D.; Shimizu, A.; Tesar, L.; Kobatake, H. and Nawano, S. (2006). Artificial Intelligence Methods Application in Liver Diseases Classification from CT Images. In 6th International Workshop on Pattern Recognition in Information Systems (ICEIS 2006) - PRIS; ISBN 978-972-8865-55-9, SciTePress, pages 146-155. DOI: 10.5220/0002444701460155

@conference{pris06,
author={Daniel Smutek. and Akinobu Shimizu. and Ludvik Tesar. and Hidefumi Kobatake. and Shigeru Nawano.},
title={Artificial Intelligence Methods Application in Liver Diseases Classification from CT Images},
booktitle={6th International Workshop on Pattern Recognition in Information Systems (ICEIS 2006) - PRIS},
year={2006},
pages={146-155},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002444701460155},
isbn={978-972-8865-55-9},
}

TY - CONF

JO - 6th International Workshop on Pattern Recognition in Information Systems (ICEIS 2006) - PRIS
TI - Artificial Intelligence Methods Application in Liver Diseases Classification from CT Images
SN - 978-972-8865-55-9
AU - Smutek, D.
AU - Shimizu, A.
AU - Tesar, L.
AU - Kobatake, H.
AU - Nawano, S.
PY - 2006
SP - 146
EP - 155
DO - 10.5220/0002444701460155
PB - SciTePress

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