Directional Binary Wavelet Patterns for Biomedical Image Indexing and Retrieval
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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.
KeywordsDirectional Binary Wavelet Patterns (DBWP) Local Binary Patterns (LBP) Image retrieval
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.
- 4.Müller, H., Lovis, C., Geissbuhler, A., Medical image retrieval—the MedGIFT project. Medical Imaging and Telemedicine, 2–7, 2005.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
- 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
- 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.Google Scholar
- 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.Google Scholar
- 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.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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar