Abstract
Image retrieval systems attempt to search through a database to find images that are perceptually similar to a query image. This work aims to develop an efficient visual-Content-based technique to search, browse and retrieve relevant images from large-scale of medical image collections Features play a vital role during the image retrieval. The various features that can be extracted are texture, color, intensity, shape, resolution, global and local features etc. In this work, we concentrate on the specific medical domain. The features such as color may not prove to be a very efficient method because the medical domain largely deals with the gray scale images. The features explored in this work are intensity, texture. The first step is to extract the texture feature and the intensity feature from the given input image. Then the both features are combined to form the single feature vector of the image by using the fusion method. The resulting image is compared to the images in the database. The N top most similar images are then retrieved from the database.
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References
Dy, J.G., Brodley, C.E., Kak, A., Broderic, L.S., Aisen, A.M.: Unsupervised feature selection applied to context based retrieval of lung images. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(3) (March 2003)
Kavitha, C., Prabhakara Rao, B., Govardhan, A.: Image Retrieval based on Color and Texture feature of the image sub block. International Journal of Computer Applications (0975– 8887) 15(7) (February 2011)
Florea, F., Barbu, E., Rogozan, A., Bensrhair, A.: Using texture based symbolic features for medical image representation. In: The 18th International Conference on Pattern Recognition (ICPRO 2006). IEEE (2006)
Youssif, A.A.A., Darwish, A.A., Mohamed, R.A.: Content based medical image retrieval based on pyramid structure wavelet. IJCSNS International Journal of Computer Science and Network Security 10(3) (March 2010)
Zare, M.R., Seng, W.C.: Integration of Color, Texture and Shape for Blood Cell Image Retrieval. In: International Conference on Biomedical Engineering, vol. 21, pp. 847–850
Mäenpää, T., Pietikäinen, M.: Texture Analysis with Local Binary Pattern. WSPC for Review Volume (May 13, 2004)
Marinai, S.: A Survey of Document Image Retrieval in Digital Libraries, Dipartimento di Sistemi e Informatica University of Florence, Italy
Unay, D., Ekin, A., Eindhoven: Intensity versus texture for medical image search and retrieval, FP6 IRonDB Project MTK-CT-2006-047217. IEEE (2008)
Kong, W.K., Zhang, D., Li, W.: Palmprint feature extraction using 2-D Gabor Filters, Department of Computing,Biometrics Research Centre,The Hong Kong Polytechnic University, HungHom, Kowloon, Hong Kong Received May 15, 2002; received in revised form January 14, 2003; accepted February 14, 2003
Veni, S., Narayanankutty, K.A.: Image Enhancement of Medical Images using Gabor Filter Bank on Hexagonal Sampled Grids. World Academy of Science, Engineering and Technology 65 (2010)
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Ramamurthy, B., Chandran, K.R., Meenakshi, V.R., Shilpa, V. (2012). CBMIR: Content Based Medical Image Retrieval System Using Texture and Intensity for Dental Images. In: Mathew, J., Patra, P., Pradhan, D.K., Kuttyamma, A.J. (eds) Eco-friendly Computing and Communication Systems. ICECCS 2012. Communications in Computer and Information Science, vol 305. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32112-2_16
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DOI: https://doi.org/10.1007/978-3-642-32112-2_16
Publisher Name: Springer, Berlin, Heidelberg
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