Abstract
In this paper we propose an approach for a feature combination helping to distinguish searched images from databases by retrieving relevant images. The retrieval effectiveness of 11 well known image features, commonly used in Content Based Image Retrieval (CBIR) systems, is investigated. We suggest a combined features approach including features’ performance comparison of 57 various medical image categories from IRMA Database. The most informative 3 features, adaptive to image categories, are defined. Based on experiments and image similarity accuracy analysis we suggest a set of 3 low level features Color Layout, Edge Histogram and DCT Coefficients. The developed approach achieves better similar images retrieval results for more image classes. The results show an accuracy improvement of 14.49% on Mean Average Precision (MAP). The comparison is done to the same type performance measure of the best individual feature in different medical image categories.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Veltkamp, R., Tanase, M.: Content-Based Image Retrieval Systems: A Survey. UU–CS 2000–34. Utrecht, The Netherlands: Utrecht University: Information and Computing Sciences (2000)
Muller, H., Michoux, N., Bandon, D., Geissbuhler, A.: A Review of Content-Based Image Retrieval Systems in Medical Applications – Clinical Benefits and Future Directions. Int. J. Medical Informatics, 1–23 (2004)
Dy, J., Brodley, C., Kak, A., Broderick, L., Aisen, A.: Unsupervised Feature Selection Applied to Content-Based Retrieval of Lung Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(3) (2003)
Hersh, W., Müller, H., Kalpathy-Cramer, J.: The ImageCLEFmed Medical Image Retrieval Task Test Collection. Proceedings of J. Digital Imaging, 648–655 (2009)
Coelho, F., Ribeiro, C.: Evaluation of Global Descriptors for Multimedia Retrieval in Medical Applications. In: Database and Expert Systems Applications (DEXA) Workshop, pp. 127–131 (2010)
Shyu, C., Pavlopoulou, C., Kak, A., Brodley, C., Broderick, L.: Using Human Perceptual Categories for Content – Based Retrieval from a Medical Image Database. Computer Vision and Image Understanding 88, 119–151 (2002)
Petrakis, E., Faloutsos, C.: Similarity searching in medical image databases. IEEE Trans. Knowledge and Data Engineering 9(3), 435–447 (1997)
Lux, M., Chatzichristofis, S.: LIRe: Lucene Image Retrieval – An Extensible Java CBIR Library. In: Proceedings of the 16th ACM International Conference on Multimedia, Vancouver, Canada, pp. 1085–1088 (2008)
Swain, M.J., Ballard, D.H.: Color Indexing. International Journal of Computer Vision 7(1), 11–32 (1991)
Chang, S.F., Sikora, T., Puri, A.: Overview of the MPEG–7 Standard. IEEE Transactions on Circuits and Systems for Video Technology 11(6), 688–695 (2001)
Deselaers, T., Keysers, D., Ney, H.: Features for Image Retrieval: An Experimental Comparison. Information Retrieval 11(2), 77–107 (2008)
Müller, H., Müller, W., Squire, D.M., Marchand-Maillet, S., Pun, T.: Performance Evaluation in Content-Based Image Retrieval: Overview and Proposals. Pattern Recognition Letters (Special Issue on Image and Video Indexing) 22(5), 593–601 (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Popova, A.A., Neshov, N.N. (2013). Combining Features Evaluation Approach in Content-Based Image Search for Medical Applications. In: Kountchev, R., Iantovics, B. (eds) Advances in Intelligent Analysis of Medical Data and Decision Support Systems. Studies in Computational Intelligence, vol 473. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00029-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-00029-9_10
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00028-2
Online ISBN: 978-3-319-00029-9
eBook Packages: EngineeringEngineering (R0)