Abstract
The paper will discuss in detail the new possibilities for making linguistic description and semantic interpretations of 64-slice spiral CT coronary vessels visualizations with the use of AI linguistic formalisms and especially ETPL(k) graph grammar. Current research shows that a significant part of diagnostic imaging, including of coronary arteries, is still difficult to automatically assess using computer analysis techniques aimed at extracting information having semantic meaning. The proposed syntactic semantic description makes it possible to intelligently model the examined structure and then to automatically find the locations of significant stenoses in coronary arteries and identify their morphometric diagnostic parameters.
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References
Lewandowski, P., Tomczyk, A., Szczepaniak, P.S.: Visualization of 3-D Objects in Medicine - Selected Technical Aspects for Physicians. Journal of Medical Informatics and Technologies 11, 59–67 (2007)
Higgins, W.E., Reinhardt, J.M.: Cardiac image processing. In: Bovik, A. (ed.) Handbook of Video and Image Processing, pp. 789–804. Academic Press, London (2000)
Sonka, M., Fitzpatrick, J.M.: Handbook of Medical Imaging. Medical Image Processing and Analysis, vol. 2. SPIE, Belligham (2004)
Wang, Y., Liatsis, P.: A Fully Automated Framework for Segmentation and Stenosis Quantification of Coronary Arteries in 3D CTA Imaging, dese. In: 2009 Second International Conference on Developments in eSystems Engineering, pp. 136–140 (2009)
Oncel, D., Oncel, G., Tastan, A., Tamci, B.: Detection of significant coronary artery stenosis with 64-section MDCT angiography. European Journal of Radiology 62(3), 394–405 (2007)
Cios, K.J., Goodenday, L.S., Merhi, M., Langenderfer, R.A.: Neural networks in detection of coronary artery disease. In: Proc. Computers in Cardiology, pp. 33–37 (1990)
Akay, Y.M., Akay, M., Welkowitz, W., Kostis, J.: Noninvasive detection of coronary artery disease using wavelet based fuzzy neural networks. IEEE EMB. Mag. 13(5), 761–764 (1994)
Cios, K.J., Goodenday, L.S., Wedding, A.: A Bayesian approach for dealing with uncertainties in detection of coronary artery stenosis using a knowledge-based system, DK. IEEE Eng. Med. Biol. Mag. 8(4), 53–58 (1989)
Ogiela, M.R., Tadeusiewicz, R.: Syntactic reasoning and pattern recognition for analysis of coronary artery images. Artificial Intelligence in Medicine 26, 145–159 (2002)
Tadeusiewicz, R., Ogiela, M.R.: Medical Image Understanding Technology. Springer, Heidelberg (2004)
Tadeusiewicz, R., Ogiela, M.R.: Structural Approach to Medical Image Understanding. Bulletin of the Polish Academy of Sciences – Technical Sciences 52(2), 131–139 (2004)
Ogiela, M.R., Tadeusiewicz, R.: Modern Computational Intelligence Methods for the Interpretation of Medical Images. Springer, Berlin (2008)
Meyer-Baese, A.: Pattern Recognition in Medical Imaging. Elsevier-Academic Press, Amsterdam (2003)
Tadeusiewicz, R., Korohoda, P.: Computer Analysis and Image Processing. Foundation of Progress in Telecommunication, Cracow (1997) (in Polish)
Tanaka, E.: Theoretical aspects of syntactic pattern recognition. Pattern Recognition 28, 1053–1061 (1995)
Pavlidis, T.: Algorithms for graphics and image processing. Computer Science Press, Rockville (1982)
Tadeusiewicz, R., Flasiński, M.: Pattern Recognition. PWN, Warsaw (1991) (in Polish)
Flasiński, M.: On the parsing of deterministic graph languages for syntactic pattern recognition. Pattern Recognition 26, 1–16 (1993)
Skomorowski, M.: A Syntactic-Statistical Approach to Recognition of Distorted Patterns. Jagiellonian University, Krakow (2000)
Get the Entire Picture, SOMATOM Sensation Cardiac 64 Brochure, Siemens medical (2004)
World Health Organization (WHO), The top ten causes of death - Fact sheet N310 (October 2008)
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Trzupek, M., Ogiela, M.R., Tadeusiewicz, R. (2011). Intelligent Image Content Description and Analysis for 3D Visualizations of Coronary Vessels. In: Nguyen, N.T., Kim, CG., Janiak, A. (eds) Intelligent Information and Database Systems. ACIIDS 2011. Lecture Notes in Computer Science(), vol 6592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20042-7_20
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DOI: https://doi.org/10.1007/978-3-642-20042-7_20
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