Autors: Popova, A. A., Neshov N. N.
Title: Combining Features Evaluation Approach in Content-Based Image Search for Medical Applications
Keywords: CBIR, image similarity search, feature selection, query by e

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.


  1. Veltkamp R. C., Tanase, M., 2002, Content-Based Image Retrieval Systems: A Survey. Tech. rep., Utrecht, Department of Computing Science, Utrecht University
  2. Muller, H., Michoux, N., Bandon, D., Geissbuhler, A, 2004, A Review of Content-Based Image Retrieval Systems in Medical Applications – Clinical Benefits and Future Directions, International Journal of Medical Informatics, Volume 73(1), pp. pp.1–23
  3. Dy, J., Brodley, C., Kak, A., Broderick, L., Aisen A., 2003, Unsupervised Feature Selection Applied to Content-Based Retrieval of Lung Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 25(3), pp. pp.373-378
  4. Hersh, W., Müller, H., Kalpathy-Cramer, J., 2009, The ImageCLEFmed Medical Image Retrieval Task Test Collection, Journal of Digital Imaging, Volume 22(6), pp. pp.648–655
  5. Coelho, F., Ribeiro, C., 2010, Evaluation of Global Descriptors for Multimedia Retrieval in Medical Applications, Database and Expert Systems Applications (DEXA) Workshop, Volume 1, pp. pp.127–131
  6. Shyu, C., Pavlopoulou, C., Kak, A., Brodley, C., Broderick L., 2002, Using Human Perceptual Categories for Content – Based Retrieval from a Medical Image Database, Computer Vision and Image Understanding, Volume 88(3), pp. pp.119–151
  7. Petrakis, E., Faloutsos, 1997, Similarity searching in medical image databases, IEEE Transactions on Knowledge and Data Engineering, Volume 9(3), pp. pp.435–447
  8. Lux, M., Chatzichristofis, S., 2008, LIRe: Lucene Image Retrieval – An Extensible Java CBIR Library, Proceedings of the 16th ACM International Conference on Multimedia, Volume 1, pp. pp. 1085–1088
  9. Swain, M. J., Ballard, D. H., 1991, Color Indexing, International Journal of Computer Vision, Volume 7(1), pp. pp.11–32
  10. Chang, S. F., Sikora, T., Puri, A., 2001, Overview of the MPEG–7 Standard, IEEE Transactions on Circuits and Systems for Video Technology, Volume 11(6), pp. pp.688–695
  11. Deselaers, T., Keysers, D., Ney, H., 2008, Features for Image Retrieval: An Experimental Comparison, Information Retrieval, Volume 11(2), pp. pp.77–107
  12. Müller, H., Müller, W., Squire, D. M., Marchand-Maillet, S., Pun, T., 2001, Performance Evaluation in Content-Based Image Retrieval: Overview and Proposals, Pattern Recognition Letters (Special Issue on Image and Video Indexing), Volume 22(5), pp. pp.593–601


, 2012, Germany, Springer-Verlag

Copyright Springer-Verlag

Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание