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Vol: 58(72) No: 1 / March 2013 Supervised Classification of Medical Ultrasound Images Using the Local Binary Pattern Operator Oana Astrid Vătămanu Department of Medical Informatics, University of Medicine and Pharmacy “Victor Babes”, Piata Eftimie Murgu, 300041 Timisoara, Romania, phone: (0040) 256-220484, e-mail: voanaastrid@yahoo.com Mihaela Ionescu Department of Medical Informatics, University of Medicine and Pharmacy “Victor Babes”, Piata Eftimie Murgu, 300041 Timisoara, Romania Gheorghe-Ioan Mihalaş Department of Medical Informatics, University of Medicine and Pharmacy “Victor Babes”, Piata Eftimie Murgu, 300041 Timisoara, Romania Keywords: Local Binary Pattern, image classification, image retrieval, ultrasound images Abstract This paper aims to present a classification and retrieval technique applied to ultrasound medical images, based on different variations of Local Binary Pattern (LBP) algorithm. Using this technique, a dedicated application builds an ultrasound image database, determining the optimum variation of LBP algorithm. These techniques can be applied to an image or to a group of images. Characterization is done through an array of values extracted by the algorithm. The application allows the characterization of an image, a set of images, determining the similarity between different images and the degree of belonging to a particular group. There are also presented several comparisons between existent variations of this algorithm, applied on the same set of ultrasound images. References [1] M. Pietikäinen, A. Hadid, G. Zhao and T. Ahonen, “Computer Vision Using Local Binary Patterns”, Springer-Verlag London Limited 2011. [2] T. Ojala, M. Pietikäinen and T. Mäenpää, “Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns.”, IEEE Trans. Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987. [3] T. Maenpaa, “The Local Binary Pattern Approach to Texture Analysis-Extensions and Applications”, Infotech Oulu and Department of Electrical and Information Engineering, University of Oulu, Finland, 2003. [4] T. Ahonen, A. Hadid, and Matti Pietik¨ainen, “Face Recognition with Local Binary Patterns”, Machine Vision Group, Infotech Oulu, University of Oulu, Finland, 2004. [5] A. Materka and M. Strzelecki, “Texture Analysis Methods – A Review”, Technical University of Lodz, Institute of Electronics, COST B11 report, Brussels, 1998. [6] C. H. Chen and L. F. Pau, “The Handbook of Pattern Recognition and Computer Vision”, 2nd Edition, World Scientific Publishing Co., 1998. [7] F. Candea, “Clasificarea imaginilor utilizand tehnici Local Binary Pattern”, Universitatea Politehnica Timisoara, 2011. [8] L. Nannia, A. Lumini and Sheryl Brahnam, “Local binary patterns variants as texture descriptors for medical image analysis”, Artificial Intelligence in Medicine 49, 2010. [9] J. Canny, “A Computational Approach to Edge Detection”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. PAMI-8, no. 6, November, 1986. [10] T. Ojala, M. Pietikäinen, D. Harwood, “A comparative study of texture measures with classification based on feature distributions”, Pattern Recognition, 1996. [11] T. Ahonen, A. Hadid, M. Pietikäinen, “Face Recognition with Local Binary Patterns”, 2001. [12] G. Zolynski, T. Braun, K. Berns, “Local Binary Pattern Based Texture Analysis in Real Time using a Graphics Processing Unit”, 2008. [13] Oana Astrid Vătămanu, Mihaela Ionescu and Gheorghe-Ioan Mihalaş, “Analysis and classification of ultrasound images using the Local Binary Pattern operator”, The 32nd International Conference on Medical Informatics, Ro-Medinf 2012, Timisoara, Romania, 2012. [14] L. Nanni, A. Lumini, S. Brahnam, “Local binary patterns variants as texture descriptors for medical image analysis”, Artif. Intell. Med. 49, 2010, 117–125. [15] http://java.sun.com/products/java-media /jai/ forDevelopers / jai1_0_1guide-unc /Geom-image-manip.doc.html. [16] http://www.tomgibara.com/computer-vision/canny-edge-detector. |