Abstract

Today content-based image retrieval (CBIR) has become one of the most active areas of research in computer vision. With rapid advances in digital imaging modalities, the use of CBIR to search for the clinically relevant and visually similar medical images is highly felt nowadays. This paper proposes a system for content based image retrieval of X-ray images. The six classes of X-ray images used for this work are from the IRMA ImageCLEFmed 2008 database. Discrete Cosine Transform (DCT) coefficients were used as features and the X-rays were classified using Support Vector Machine (SVM). The classified images along with the features were stored in the database using hierarchical index structure. Euclidean distance is used as the metric for retrieving the top three images from the database relevant to the given query image

References

• Liu Y, Zhang D, Lu G, Ma WY. A survey of content-based image retrieval with high-level semantics. Pattern Recognition 2007; 40:262–82.
• Lehmann TM, Guld MO, Deselaers T, Keysers D, Schubert H, Spitzera K, et al. Automatic categorization of medical images for content-based retrieval and data mining. Comput Med Image Graph 29:143–55, 2005.
• Rafael C. Gonzalez& Richard E. Woods, "Digital Image Processing", Second Edition, 2005.
• Rafael C. Gonzalez, Richard E. Woods & Steven L. Eddins, "Digital Image Processing Using MATLAB", Pearson Education 2007.
• Principal component analysis: www. fon. hum. uva. nl/praat/manual/principal_component_analysis. html
• V. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.
• SVM - Support Vector Machines: www. dtreg. com. svm. html
• Marina Sokolova, Nathalie Japakowicz and Stan Szpakowicz, "Beyond Accuracy, F-score and ROC: a family of Discriminant measures for performance evaluation",www. etud. iro. umontreal. ca/nsokolovm/australia06. pdf/ 2006.
• Rahman MM, Bhattacharya P, Desai BC. A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback. IEEE Trans Inf Technol Biomed 2007;11(1):58–69.
• Greenspan H, Pinhas AT. Medical image categorization and retrieval for PACS using the GMM-KL framework. IEEE Trans InfTechnol Biomed 2007;11(2):190–202.
• Jain AK, Duin PW, Mao J. Statistical pattern recognition—a review. IEEE Trans Pattern Anal Mach Intell 2000; 22(1):4–36.
• K. Vaidehi T. S. Subashini V. Ramalingam S. Palanivel M. Kalaimani, "Transform based approaches for Palmprint Identification", International Journal of Computer Applications (0975 – 8887) Volume 41– No. 1, March 2012.
• Lehmann, T "IRMA X- Ray Library url: http://irma-project. org.
• D. C. Tao, X. O. Tang, X. L. Li, X. D. Wu, Asymmetric bagging and random space for support vectormachines-basedrelevance feedback in image retrieval, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, n07, July 2006.
• Content based Image Retrieval for Medical Images using Canny Edge Detection Algorithm,International Journal of Computer Applications (0975 – 8887)Volume 17– No. 6, March 2011.
• Datta,R. ,Joshi,D. ,Li,J. ,andWang,J. Z. Image retrieval: Ideas, influences, and trends of the new age. ACM Comput. Surv. 40, 2, Article 5 (April 2008).
• Fesharaki, Nooshin Jafari, and Hossein Pourghassem. "Medical X-ray Images Classification Based on Shape Features and Bayesian Rule. " In Computational Intelligence and Communication Networks (CICN), 2012 Fourth International Conference on, pp. 369-373. IEEE, 2012.
• Zare, Mohammad Reza, Woo Chaw Seng, and Ahmed Mueen. "AUTOMATIC CLASSIFICATION OF MEDICAL X-RAY IMAGES. " Malaysian Journal of Computer Science 26 (2013): 1.
• Ghofrani, Fatemeh, Mohammad Sadegh Helfroush, Mahmoud Rashidpour, and Kamran Kazemi. "Fuzzy-Based Medical X-ray Image Classification. " Journal of medical signals and sensors 2, no. 2 (2012): 73.