IJRCS – Volume 3 Issue 2 Paper 3


Author’s Name : S.Iswarya  | R.Kavitha

Volume 03 Issue 02  Year 2016  ISSN No:  2349-3828  Page no: 11-14



An image retrieval system is used in large database system for searching and retrieving images. Indexing and retrieval of the images efficiently is an important task. Medical images maintaining using text or numbers is a difficult task and time taking. Numbering and retrieval of images can be done through query by text and query by image also termed as content based image retrieval. Content-based image retrieval takes in visual contents to search images from databases according to user interest. Texture is one among the efficient features for image retrieval. In texture based retrieval methods like local binary pattern and local mesh pattern the images are retrieved based on relationship among the pixel values. Based on local binary pattern local mesh pattern is calculated by the relationship between the neighbouring pixels. The efficiency of the system is enhanced or increased by combining with gabor transform. The gabor local mesh pattern and local binary pattern histograms are combined to get a feature vector. Effective comparisons algorithm is then used to retrieve the image of closest proximity. The mesh pattern is capable of extracting more edge information so the efficiency is increased for biomedical image application.


Gabor transform(GT), local binary pattern (LBP), local mesh patterns (LMeP), gabor local mesh pattern (GLMeP).


  1. Andre B, Vercauteren T, Buchner A. M., Wallace M. B. and Ayache N. (2012), ‘Learning semantic and visual similarity for endomicroscopy video retrieval’, IEEE Transaction. Medical Imaging, volume 31, no. 6, pp. 1276–1288.
  2. Cai W, Feng D. D. and Fulton R. (2000), ‘Content-based retrieval of dynamic PET functional images’, IEEE Transaction. Information Technology, Biomedical, volume 4, no. 2, pp. 152–158.
  3. Guo Z, Zhang L. and Zhang D. (2010), ‘Rotation invariant texture classification using LBP variance with global matching’, Pattern Recognition, volume 43, pp. 706–716.
  4. Liu Y, Zhang D, Lu G. and Ma W.Y. (2007), ‘A survey of content-based image retrieval with high level semantics’ , Pattern Recognition, volume 40, pp. 262–282.
  5. Muller H, Rosset A., Vallet J. P. and Geisbuhler A. (2004), ‘Comparing feature sets for content-based image retrieval in a medical case database’, Medical Imaging, Imaging Information, San Diego, USA, pp. 99–109.
  6. Murala S, Maheshwari R. P. and Balasubramanian R. (2012), ‘Directional local extrema patterns A new descriptor for content based image retrieval’, International Journal .Multimedia Information Retrieval, volume 1, pp. 191–203.
  7. Murala S, Maheshwari R. P. and Balasubramanian R. (2012), ‘Local maximum edge binary patterns A new descriptor for image retrieval and object tracking’ , Signal Processing, volume 92, pp. 1467–1479.
  8. Nakayama R, Abe H., Shiraishi J. and Doil K. (2011), ‘Evaluation of objective Similarity measures for selecting similar images of mammographic lesions’, Journal Digital Imaging.volume 24, no. 1, pp. 75–85.
  9. Ojala T, Pietikainen M. and Harwood D. (1996), ‘A comparative study of texture measures with classification based on feature distributions’, Pattern Recognition, volume 29, no. 1, pp. 51–59.
  10. Quddus A. and Basir O. (2012), ‘Semantic image retrieval in magnetic resonance brain volumes’, IEEE Transaction. Information Technology, Biomedical, volume 16, no. 3, pp. 348–355.
  11. Peng S, Kim D., Lee S., Lim M. (2010), ‘Texture feature extraction on uniformity estimation for local brightness and structure in chest CT images’, Journal Computer Biological Medical, volume 40, pp. 931–942.
  12. Rahman M. M., Antani S. K. and Thoma G. R. (2011), ‘A learning-based similarity fusion and filtering approach for biomedical image retrieval using SVM classification and relevance feedback’ , IEEE Transaction. Information Technology, Biomedical, volume 15, no. 4, pp. 640–646.
  13. Rui Y. and Huang T. S. (1999), ‘Image retrieval Current techniques, promising Directions and open issues’, Journal Visual Communication. Image Representation, volume 10,pp. 39–62.
  14. Scott G. and Shyu C.R. (2007), ‘Knowledge-Driven multidimensional indexing Structure for biomedical media database retrieval’,IEEE Transaction. Information Technology Biomedical, volume 11, no. 3, pp. 320–331.
  15. Takala V., Ahonen T. and Pietikainen M. (2005), ‘Block-based methods for image retrieval using local binary patterns’ , Scandinavian Conference. Image Analysis,USA, volume 3450, pp. 882–891.
  16. Traina A., Castanon C. and Traina C. (2003), ‘Multiwavemed system for medical image retrieval through wavelets transformations’, IEEE Symposium. Computer Based Medical System, New York, USA, pp. 150–155.
  17. Traina A., Felipe J. C. and Traina C. (2003), ‘Retrieval by content of medical images using texture for tissue identification’, IEEE Symposium. Computer Based Medical System, New York, USA, pp. 175–180.
  18. Tan X. and Triggs B. (2010), ‘Enhanced local texture feature sets for face recognition under difficult lighting conditions’, IEEE Transaction. Image Processing, volume 19, no. 6, pp. 1635–1650.
  19. Yao C.H. and Chen S.Y. (2003), ‘Retrieval of translated, rotated and scaled color textures’ ,Pattern Recognition, volume 36, pp. 913–929.
  20. Zhang B., Gao Y., Zhao S. and Liu J. (2010), ‘Local derivative pattern versus local binary pattern Face recognition with higher-order local pattern descriptor’ IEEE Transaction. Image Processing, volume 19, no. 2, pp. 533–544.