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Vol: 53(67) No: 2 / June 2008 Medical Images Used in a Diagnosis System Adriana Adriana Albu Department of Automation and Applied Informatics, “Politehnica” University of Timisoara, Faculty of Automation and Computers, 2, Vasile Parvan Blvd, 300223, Timisoara, Romania, e-mail: adriana.albu@aut.upt.ro Loredana Ungureanu Department of Automation and Applied Informatics, “Politehnica” University of Timisoara, Faculty of Automation and Computers, 2, Vasile Parvan Blvd, 300223, Timisoara, Romania, e-mail: loredana.ungureanu@aut.upt.ro Keywords: medical images, liver, texture, co-occurrence matrices, artificial neural networks Abstract The liver has always been the organ representing the greatest challenge to the radiologists because of the difficulty to appreciate the morphological changes induced by the illness. The usage of images provided by computed tomography, magnetic resonance imaging, ultrasound examination or angiography is very important to establish the illness stage and to predict its evolution. This paper describes a system developed to make some predictions regarding the liver’s diseases that can be detected by computed tomography and it is a clear example of interdisciplinary research. References [1] Liver Cancer Network: www.livercancer.com. [2] Cancer Group Institute: www.cancergroup.com. [3] M. Wasilewski: “Active Contours using Level Sets for Medical Image Segmentation”, University of Waterloo, August 2004. [4] A. Vlaicu: “Prelucrarea digitală a imaginilor”, Editura Albastră, Cluj-Napoca, 1997. [5] A. Rexhepi, A. Rosenfeld, F. Mokhtarian: “Extracting Boundaries from Images by Comparing Cooccurrence Matrices”, Proceedings of the VII-th Digital Image Computing: Techniques and Applications, 10-12 Dec. 2003, Sydney. [6] S. A. Karkanis, G. D. Magoulas, N. G. Theofanous: “Image Recognition and Neural Networks: Intelligent Systems for the Improvement of Imaging Information”, Minimal Invasive Theraphy and Allied Technologies, vol. 9, pp. 225-230, 2000. [7] S. A. Karkanis, D. K. Iakovidis, D. E. Maroulis, G. D. Magoulas, N. G. Theofanous: “Tumor recognition in endoscopic video images using Artificial Neural Networks Architectures”, Ed. Los Alamitos, CA: IEEE Press, 2000. [8] A. Albu, A. Drăgulescu: “Medical Diagnosis using Artificial Neural Networks”, Proceedings of the XI-th International Conference on Vibration Engineering, vol. 50(64), pp.1-4, Timişoara, 2005. |