Skip to main content

Directional Binary Wavelet Patterns for Biomedical Image Indexing and Retrieval

Abstract

A new algorithm for medical image retrieval is presented in the paper. An 8-bit grayscale image is divided into eight binary bit-planes, and then binary wavelet transform (BWT) which is similar to the lifting scheme in real wavelet transform (RWT) is performed on each bitplane to extract the multi-resolution binary images. The local binary pattern (LBP) features are extracted from the resultant BWT sub-bands. Three experiments have been carried out for proving the effectiveness of the proposed algorithm. Out of which two are meant for medical image retrieval and one for face retrieval. It is further mentioned that the database considered for three experiments are OASIS magnetic resonance imaging (MRI) database, NEMA computer tomography (CT) database and PolyU-NIRFD face database. The results after investigation shows a significant improvement in terms of their evaluation measures as compared to LBP and LBP with Gabor transform.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

References

  1. Mueen, A., Zainuddin, R., and Sapiyan Baba, M., MIARS: A medical image retrieval system. J. Med. Syst. 34:859–864, 2010.

    Article  Google Scholar 

  2. Chu, W., Hsu, C., Cardenas, C., and Taira, R., Acknowledge-based image retrieval with spatial and temporal constructs. IEEE Trans. Knowl. Data Eng. 10(6):872–888, 1998.

    Article  Google Scholar 

  3. Shyu, C., Kak, A., Kosaka, A., Aisen, A., and Broderick, L., ASSERT: A physician-in-the-loop content-based inage retrieval system for HRCT image databases. Comput. Vis. Image Underst. 75:111–132, 1998.

    Article  Google Scholar 

  4. Müller, H., Lovis, C., Geissbuhler, A., Medical image retrieval—the MedGIFT project. Medical Imaging and Telemedicine, 2–7, 2005.

  5. Rui, Y., and Huang, T. S., Image retrieval: Current techniques, promising directions and open issues. J. Vis. Commun. Image Represent. 10:39–62, 1999.

    Article  Google Scholar 

  6. Smeulders, A. W. M., Worring, M., Santini, S., Gupta, A., and Jain, R., Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22(12):1349–1380, 2000.

    Article  Google Scholar 

  7. Kokare, M., Chatterji, B. N., and Biswas, P. K., A survey on current content based image retrieval methods. IETE J. Res. 48(3&4):261–271, 2002.

    Google Scholar 

  8. Lew, M. S., Sebe, N., Djerba, C., and Jain, R., Content-based multimedia information retrieval: State of the art and challenges. ACM Trans. Multimedia Comput., Commun., Appl. 2(1):1–19, 2006.

    Article  Google Scholar 

  9. Liu, Y., Zhang, D., Guojun, Lu, and Ma, W.-Y., Asurvey of content-based image retrieval with high-level semantics. J. Pattern Recognition 40:262–282, 2007.

    MATH  Article  Google Scholar 

  10. Müller, H., Michoux, N., Bandon, D., and Geisbuhler, A., A review of content-based image retrieval systems in medical applications–Clinical benefits and future directions. J. Med. Inf. 73(1):1–23, 2004.

    Article  Google Scholar 

  11. Manjunath, K. N., Renuka, A., and Niranjan, U. C., Linear models of cumulative distribution function for content-based medical image retrieval. J. Med. Syst. 31:433–443, 2007.

    Article  Google Scholar 

  12. Woo Chaw Seng, and Seyed Hadi Mirisaee, Evaluation of a content-based retrieval system for blood cell images with automated methods. J. Med. Syst. doi:10.1007/s10916-009-9393-3.

  13. Fahimeh Sadat Zakeri, Hamid Behnam, Nasrin Ahmadinejad, Classification of benign and malignant breast masses based on shape and texture features in sonography images. J. Med. Syst. doi:10.1007/s10916-010-9624-7.

  14. Yang, L., Student, Jin, R., Mummert, L., Sukthankar, R., Goode, A., Zheng, B., Hoi, S. C. H., and Satyanarayanan, M., A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 32(1):33–44, 2010.

    Google Scholar 

  15. Quellec, G., Lamard, M., Cazuguel, G., Cochener, B., and Roux, C., Wavelet optimization for content-based image retrieval in medical databases. J. Med. Imag. Anal. 14:227–241, 2010.

    Article  Google Scholar 

  16. Traina, A, Castanon, C, Traina, C Jr., Multiwavemed: A system for medical image retrieval through wavelets transformations. Proc. 16th IEEE Symp. Comput.-Based Med. Syst., New York, USA, 150–155, 2003.

  17. Felipe, J. C., Traina, A. J. M., Traina, C. Jr., Retrieval by content of medical images using texture for tissue identification. 16th IEEE Symp. Comput.-Based Med. Syst., New York, USA, 175–180, 2003.

  18. Müller, H., Rosset, A., Vallét, J. -P., Geisbuhler, A., Comparing feature sets for content-based image retrieval in a medical case database. Proc. SPIE Med. Imag., PACS Imag. Inf., San Diego, USA, 99–109, 2004.

  19. Swanson, M. D., and Tewfik, A. H., A binary wavelet decomposition of binary images. IEEE Trans. Image Process. 5:1637–1650, 1996.

    Article  Google Scholar 

  20. Kamstra, L., The design of linear binary wavelet transforms and their application to binary image compression. IEEE Inter. Conf. Image Processing, ICIP’03, 241–244, 2003.

  21. Kamstra, L., Nonlinear binary wavelet transforms and their application to binary image compression. Proc. 2003 IEEE Inter. Conf. Image Processing, ICIP’02, 3 593–596, 2002.

  22. Gerek, Ö. N., Çetin, A. E., Tewfik, A. H., Subband coding of binary textual images for document retrieval. Proc. 2003 IEEE Inter. Conf. Image Processing, ICIP’96, 899–902, 1996.

  23. Pan, H., Jin, L.-Z., Yuan, X.-H., Xia, S.-Y., and Xia, L.-Z., Context-based embedded image compression using binary wavelet transform. J. Image Vision Computing 28:991–1002, 2010.

    Article  Google Scholar 

  24. Pan, H., Siu, W. C., and Law, N. F., Lossless image compression employing binary wavelet transform. IET Image Process. 1(4):353–362, 2007.

    Article  Google Scholar 

  25. Law, N. F., and Siu, W. C., A filter design strategy for binary field wavelet transform using the perpendicular constraint. J. Signal Process. 87(11):2850–2858, 2007.

    MATH  Article  Google Scholar 

  26. Ojala, T., Pietikainen, M., and Harwood, D., A comparative sudy of texture measures with classification based on feature distributions. J. Pattern Recognition 29(1):51–59, 1996.

    Article  Google Scholar 

  27. Ojala, T., Pietikainen, M., and Maenpaa, T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7):971–987, 2002.

    Article  Google Scholar 

  28. Pietikainen, M., Ojala, T., Scruggs, T., Bowyer, K. W., Jin, C., Hoffman, K., Marques, J., Jacsik, M., and Worek, W., Overview of the face recognition using feature distributions. J. Pattern Recognition 33(1):43–52, 2000.

    Article  Google Scholar 

  29. Li, M., and Staunton, R. C., Optimum Gabor filter design and local binary patterns for texture segmentation. J. Pattern Recognition 29:664–672, 2008.

    Article  Google Scholar 

  30. Guo, Z., Zhang, L., and Zhang, D., Rotation invariant texture classification using LBP variance with global matchning. J. Pattern Recognition 43:706–716, 2010.

    MATH  Article  Google Scholar 

  31. Liao, S., Law, M. W. K., and Chung, A. C. S., Dominant local binary patterns for texture classification. IEEE Tans. Image Proc. 18(5):1107–1118, 2009.

    Article  Google Scholar 

  32. Guo, Z., Zhang, L., and Zhang, D., A completed modeling of local binary pattern operator for texture classification. IEEE Tans. Image Proc. 19(6):1657–1663, 2010.

    Article  Google Scholar 

  33. Peng, S., Kim, D., Lee, S., and Lim, M., Texture feature extraction on uniformity estimation for local brightness and structure in chest CT images. J. Compt. Bilogy Medic. 40:931–942, 2010.

    Article  Google Scholar 

  34. Unay, D., Ekin, A., and Jasinschi, R. S., Local structure-based region-of-interest retrieval in brain MR images. IEEE Trans. Infor. Tech. Biomedicine 14(4):897–903, 2010.

    Article  Google Scholar 

  35. Sørensen, L., Shaker, S. B., and de Bruijne, M., Quantitative analysis of pulmonary emphysema using local binary patterns. IEEE Trans. Medical Imaging 29(2):559–569, 2010.

    Article  Google Scholar 

  36. Ahonen, T., Hadid, A., and Pietikainen, M., Face description with local binary patterns: Applications to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12):2037–2041, 2006.

    Article  Google Scholar 

  37. Zhao, G., and Pietikainen, M., Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6):915–928, 2007.

    Article  Google Scholar 

  38. Ning, J., Zhang, L., Zhang, D., and Chengke, W., Robust object tracking using joint color-texture histogram. Int. J. Pattern Recogn. Artif. Intell. 23(7):1245–1263, 2009.

    Article  Google Scholar 

  39. Nanni, L., and Lumini, A., Local binary patterns for a hybrid fingerprint matcher. J. Pattern Recognition 41:3461–3466, 2008.

    MATH  Article  Google Scholar 

  40. Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., and Buckner, R. L., Open access series of imaging studies (OASIS): Crosssectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cogn. Neurosci. 19(9):1498–1507, 2007.

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the Ministry of Human Resource and Development India under grant MHR-02-23-200 (429). The authors would like to thank the anonymous reviewers for insightful comments and helpful suggestions to improve the quality, which have been incorporated in this manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Subrahmanyam Murala.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Murala, S., Maheshwari, R.P. & Balasubramanian, R. Directional Binary Wavelet Patterns for Biomedical Image Indexing and Retrieval. J Med Syst 36, 2865–2879 (2012). https://doi.org/10.1007/s10916-011-9764-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-011-9764-4

Keywords

  • Directional Binary Wavelet Patterns (DBWP)
  • Local Binary Patterns (LBP)
  • Image retrieval