Artificial Intelligence Methods Application in Liver Diseases Classification from CT Images

Daniel Smutek, Akinobu Shimizu, Ludvik Tesar, Hidefumi Kobatake, Shigeru Nawano

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 distinguishing more types of liver diseases from CT images and its further integration to PACS and hospital information systems.

References

  1. Bilello M, Gokturk SB, Desser T, Napel S, Jeffrey RB, Jr., Beaulieu CF. Automatic detection and classification of hypodense hepatic lesions on contrast-enhanced venousphase CT. Med.Phys. 2004; 31: 2584-93.
  2. Gletsos M, Mougiakakou SG, Matsopoulos GK, Nikita KS, Nikita AS, Kelekis D. A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier. IEEE Trans.Inf.Technol.Biomed. 2003; 7: 153- 62.
  3. Klein HM, Klose KC, Eisele T, Brenner M, Ameling W, Gunther RW. [The diagnosis of focal liver lesions by the texture analysis of dynamic computed tomograms]. Rofo 1993; 159: 10-5.
  4. Smutek D, Sara R, Sucharda P, Tjahjadi T, Svec M. Image Texture Analysis of Sonograms in Chronic Inflammations of Thyroid Gland. Ultrasound in Medicine and Biology 2003; 29: 1531-43.
  5. Takayasu K, Muramatsu Y, Mizuguchi Y, Moriyama N, Ojima H. Imaging of early hepatocellular carcinoma and adenomatous hyperplasia (dysplastic nodules) with dynamic ct and a combination of CT and angiography: experience with resected liver specimens. Intervirology 2004; 47: 199-208.
  6. Haralick RM, Shapiro LG. Computer and Robot Vision. Reading MA : Addison-Wesley, 1992.
  7. Julesz B, Gilbert EN, Shepp LA, Frish HL. Inability of humans to discriminate between visual textures that agree in second-order statistics---revisited. Perception 1973; 2: 391-405.
  8. Muzzolini R, Yang YH, Pierson R. Texture Characterization using Robust Statistics. Pattern Recognition 1994; 27: 119-34.
  9. Muzzolini R, Yang YH, Pierson R. Multiresolution Texture Segmentation with Application to Diagnostic Ultrasound Images. IEEE Transactions on Medical Imaging 1993; 12: 108- 23.
  10. Bishop CM. Neural Networks for Pattern Recognition. Oxford: University Press, 1997.
  11. Scott D. Multivariate Density Estimation. New York: Wiley, 1992.
  12. Rohlfing T, Pfefferbaum A, Sullivan EV, Maurer CR. Information Fusion in Biomedical Image Analysis: Combination of Data vs. Combination of Interpretations. 2005.
  13. Kittler J, Hatef M, Duin RPW, Matas J. On Combining Classifiers. IEEE Transactions on PAMI 1998; 20: 226-39.
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Paper Citation


in Harvard Style

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 - Volume 1: PRIS, (ICEIS 2006) ISBN 978-972-8865-55-9, pages 146-155. DOI: 10.5220/0002444701460155


in Bibtex Style

@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 - Volume 1: PRIS, (ICEIS 2006)},
year={2006},
pages={146-155},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002444701460155},
isbn={978-972-8865-55-9},
}


in EndNote Style

TY - CONF
JO - 6th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2006)
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