Pattern Recognition Techniques Applied to Biomedical Patterns

Authors: Giovanni Luca Masala

Abstract:

Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.

Keywords: Computer Aided Detection, mammary tumor, pattern recognition, dissimilarity

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1056777

References:

[1] O. Duda, P. E. Hart, D. G. Stark, “Pattern Classification“, second edition, A Wiley-Interscience Publication John Wiley & Sons, 2001.
[2] S. Haykin “Neural Networks - A comprehensive foundation", second edition, Prentice Hall, 1999.
[3] T. D. Sanger , “Optimal Unsupervised Learning in a Single-Layer Linear Feedforward Neural Network", Neural Networks, vol. 2, pp. 459-473, 1989.
[4] M. A. Kramer, “Nonlinear Principal Component Analysis Using Autoassociative Neural Networks", AiCh Journal, vol. 37, No. 2, 1991.
[5] Aapo Hyv┬¿arinen, Erkki Oja, “Independent Component Analysis: Algorithms and Applications", Neural Networks Research Centre, Helsinki University of Technology, Finland, “Neural Networks", 13 (4- 5):411-430, 2000.
[6] H. Gupta, A. K Agrawal, T. Pruthi, C. Shekhar, R. Chellappa, "An Experimental Evaluation of Linear and Kernel-Based Methods for Face Recognition," wacv, p. 13, Sixth IEEE Workshop on Applications of Computer Vision, 2002.
[7] S. Serpico, G. Vernazza, “Teorie e tecniche del riconoscimento", CUSL “Il gabbiano", 1997.
[8] Massimo Buscema & Semeion Group, “Reti Neurali artificiali e sistemi sociali complessi", volume 1 Teoria e modelli 1409.1, Franco Angeli, 1999.
[9] E. Pekalska, R.P.W. Duin, R.P.W. and P.Paclik, "Prototype Selection for Dissimilarity-based Classifiers", Pattern Recognition, vol. 39, no. 2, pp. 189-208, February 2006.
[10] V. N. Vapnik. “Statistical Learning Theory. Wiley", New York , 1998.
[11] M. Pontil, A. Verri “Properties of Support Vector Machines", Neural Computation, Vol. 10, pp 955-974, 1998.
[12] S. J. Russel, P.Norvig, “Artificial Intelligence. A modern approach", UTET, 1998.
[13] V. N. Vapnik. “Statistical Learning Theory. Wiley", New York , 1998.
[14] M. Pontil, A. Verri “Properties of Support Vector Machines", Neural Computation, Vol. 10, pp 955-974, 1998.
[15] N. Cristianini, J. Shave-Taylor. “An Introduction to Support Vector Machine (and other kernel-based learning methods)". Cambridge University Press, 2000.
[16] SVM_light software is available in the following location :ftp://ftp-ai.cs.unidortmund.de/pub/Users/thorsten/svm_light/current/ svm_light.tar.gz
[17] T. Joachims, Text Categorization with Support Vector Machines: Learning with Many Relevant Features, Proc. 10th European Conf. Machine Learning (ECML), Springer-Verlag, 1998.
[18] T. Mitchell “Machine Learning" , McGraw-Hill, 1997.
[19] Bottigli et al, Search of Microcalcification clusters with the CALMA CAD station. The International Society for Optical Engineering (SPIE) 4684: 1301-1310, 2002
[20] F. Fauci, S. Bagnasco, R. Bellotti, D. Cascio, S. C. Cheran, F. De Carlo, G. De Nunzio, M. E. Fantacci, G. Forni, A. Lauria, E.Lopez Torres, R. Magro, G. L. Masala, P. Oliva, M. Quarta, G. Raso, A. Retico, S.Tangaro, Mammogram Segmentation by Contour Searching and Massive Lesion Classification with Neural Network, Proc. IEEE Medical Imaging Conference, October 16-22 2004, Rome, Italy; M2- 373/1-5, 2004.
[21] U. Bottigli, B. Golosio, G. L. Masala, P. Oliva, S. Stumbo, D. Cascio, F. Fauci, R. Magro, G. Raso, R. Bellotti, F. De Carlo, S.Tangaro, I. De Mitri, G. De Nunzio, M. Quarta, A. Preite Martinez, P. Cerello, S. C. Cheran, E.Lopez Torres “Dissimilarity Application for Medical Imaging Classification" on proceedings of The 9th World Multi-Conference on Systemics, Cybernetics and Informatics WMSCI 2005, Orlando 10-13 July 2005, vol III pag 258-262, 2005.
[22] G. Masala, B. Golosio, D. Cascio, F. Fauci, S. Tangaro, M. Quarta, S. C Cheran, E. L. Torres, “Classifiers trained on dissimilarity representation of medical pattern: a comparative study" on Nuovo Cimento C, Vol 028, Issue 06, pp 905-912 , 2005.
[23] S.R. Amendolia, G. Cossu, M. L. Ganadu, B. Golosio, G.L. Masala, G.M. Mura “A Comparative study of K-Nearest Neighbour, Support Vector Machine and Multi-Layer Perceptron for Thalassemia Screening" on “Chemometrics and intelligent laboratory system" ;69:13-20, 2003.
[24] S.R Amendolia , A. Brunetti, P.Carta, G. Cossu, M.L. Ganadu, B. Golosio, G.M. Mura, M.G. Pirastru, A Real-Time Classification System of Thalassemic Pathologies Based on Artificial Neural Networks Medical Decision Making; 22:18-26, 2002.
[25] Timp S., Karssemeijer N., A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography, Medical Physics: 31; 958-971, 2004.
[26] Baydush A.H., Catarious D.M., Abbey C.K., Floyd C.E., Computer aided detection of masses in mammography using subregion Hotelling observers, Medical Physics: 30; 1781-1787, 2003.
[27] Tourassi G.D., Vargas-Voracek R., Catarious D.M. Jr, Floyd C.E. Jr, Computer-assisted detection of mammographic masses: A template matching scheme based on mutual information, Medical Physics: 30 (8); 2123-2130, 2003.
[28] Antonie M.L., Zaiane O.R., Coman A., Application of data mining techniques for medical image classification, Proc. of II Int. Work. On Multimedia Data Mining, USA, 2001.
[29] Vyborny CJ., Giger ML., Computer vision and artificial intelligence in mammography, AJR: 162; 699-708, 1994.
[30] Lai S., Li X., Bischof W., On techniques for detecting circumscribed masses in mammograms", IEEE Transaction on Medical Imaging: 8(4); 377-386, 1989.
[31] Hanley JA, McNeil B, The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology: 143; 29-36, 1982.
[32] Hanley JA, McNeil B, A method of comparing the areas under receiver operating characteristic curves derived from the same cases, Radiology: 148; 839-843, 1983.