Medical Pattern Understanding and Cognitive Analysis in .NET

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Medical Pattern Understanding and Cognitive Analysis
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Table 14.5 Production set defining changes in the spinal cord. Lesion Dilatation Grammar rules 1.LESION ENLARGEMENT 2.ENLARGEMENT E H N E N EH 3.LESION NARROWING 4.NARROWING N H E N E N H 5.H h h H 6.E e e E 7.N n n N Semantic actions Lesion = enlargement Lesion = narrowing wsym = wsym + wh hsym = hsym + hh
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Figure 14.6 Results of disease symptom recognition and understanding in the images of the spinal cord.
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8. Conclusions
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The presented methods of structural cognitive analysis are basically an attempt at automating the specifically human process of understanding the medical meaning of various organ shapes on a digital image; they are not only an attempt at simple recognition. In particular, a diagnosis may result from such an automatic understanding of shape; it is also possible to draw other numerous medical conclusions. This information may supply a method of treatment, that is, different types of therapy may be recommended depending on the shape and pathological localization described in the grammar. The results obtained from the application of the characterized methods confirm the immense potential of syntactical methods in the diagnosis of cardiac ischemic diseases, urinary tract disabilities and inflammation and neoplasm processes in the pancreas, as well as lesions of the central nervous system. The syntactic methods of pattern recognition presented in this chapter have many applications in the field of artificial intelligence and medical IT, especially in the fields of computer medical imaging
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and computer-aided diagnosis. The methods, originating from mathematical linguistics, allow us not only to diagnose and create formal and advanced descriptions for complicated shapes of disease symptoms carrying diagnostic information. They can also be used to create intelligent computer systems constructed for the purpose of image perception: allowing us to obtain a definition and machineinterpretation of the semantic contents of the examined image. These systems may assist the operation of medical robots widely used in the operational field in various surgeries. They can also constitute an integral part of CAD systems or intelligent information systems managing pictorial medical databases located (scattered) in various places [36 38].
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[1] Albus, J. S. and Meystal, A. M. and Aleksander, M. Engineering of Mind: An Introduction to the Science of Intelligent Systems, John Wiley & Sons, Inc., New York, 2001. [2] Bankman, I. (Ed.) Handbook of Medical Imaging: Processing and Analysis, Academic Press, 2002. [3] Davis, L. S. (Ed.) Foundations of Image Understanding, Kluwer Academic Publishers, Norwell, 2001. [4] Duda, R. O., Hart, P. E. and Stork, D. G. Pattern Classification, 2nd edition, John Wiley & Sons, Inc., New York, 2001. [5] Leondes, C. T. (Ed.) Image Processing and Pattern Recognition, Academic Press, San Diego, 1998. [6] Ogiela, M. R. and Tadeusiewicz, R. Image Understanding Methods in Biomedical Informatics and Digital Imaging, Journal of Biomedical Informatics, 34(6), pp. 377 386, 2001. [7] Khan, M. G. Heart Disease Diagnosis and Therapy, Williams & Wilkins, Baltimore, 1996. [8] Ogiela, M. R. and Tadeusiewicz, R. Syntactic reasoning and pattern recognition for analysis of coronary artery images, Artificial Intelligence in Medicine, 26, pp. 145 159, 2002. [9] Mandal, A. K. and Jennette, J. Ch. (Eds) Diagnosis and Management of Renal Disease and Hypertension, Carolina Academic Press, 1994. [10] Silvus, S. E., Rohrmann, Ch. A. and Ansel, H. J. Text and Atlas of Endoscopic Retrograde Cholangiopancreatography. Igaku-Shain, New York, 1995. [11] Tadeusiewicz, R. and Ogiela, M. R. Artificial Intelligence Techniques in Retrieval of Visual Data Semantic Information, in Menasalvas, E., Segovia, J., Szczepaniak, P. S. (Eds), Advances in Web Intelligence, Lecture Notes in Artificial Intelligence, 2663, Springer Verlag, pp. 18 27, 2003. [12] Skomorowski, M. Use of random graphs for scene analysis, Machine Graphics & Vision, 7, pp. 313 323, 1998. [13] Tadeusiewicz, R. and Flasinski, M. Pattern Recognition, Polish Scientific Publisher, Warsaw, 1991. [14] Ogiela, M. R. and Tadeusiewicz, R. Nonlinear Processing and Semantic Content Analysis in Medical Imaging, Proceedings of IEEE International Symposium on Intelligent Signal Processing, Budapest, pp. 243 247, 2003. [15] Ogiela, M. R. and Tadeusiewicz, R. Advanced image understanding and pattern analysis methods in Medical Imaging, Proceedings of the Fourth IASTED International Conference on Signal and Image Processing (SIP 2002), Kaua i, Hawaii, USA, pp. 583 588, 2002. [16] Ogiela, M. R., Tadeusiewicz, R. and Ogiela, L. Syntactic Pattern Analysis in Visual Signal Processing and Image Understanding, The International Conference on Fundamentals of Electronic Communications and Computer Science ICFS 2002, Tokyo, Japan, pp. 13:10 13:14, 2002. [17] Meyer-Baese, A. Pattern Recognition in Medical Imaging, Elsevier Academic Press, 2004. [18] Ogiela, M. R. and Tadeusiewicz, R. Visual Signal Processing and Image Understanding in Biomedical Systems, Proceedings of the 2003 IEEE International Symposium on Circuits and Systems, 5, pp. V-17 V-20, 2003. [19] Le , Z., Tadeusiewicz, R. and Le , M. Shape Understanding: Knowledge Generation and Learning, s s Proceedings of the Seventh Australian and New Zealand Intelligent Information Systems Conference (ANZIIS 2001), Perth, Western Australia, pp. 189 195, 2001. [20] Tadeusiewicz, R. and Ogiela, M. R. Automatic Understanding Of Medical Images New Achievements In Syntactic Analysis Of Selected Medical Images, Biocybernetics and Biomedical Engineering, 22(4), pp. 17 29, 2002. [21] Ogiela, M. R. and Tadeusiewicz, R. Artificial Intelligence Structural Imaging Techniques in Visual Pattern Analysis and Medical Data Understanding, Pattern Recognition, 36(10), pp. 2441 2452, 2003.
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