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Novel Approaches of Evaluating Texture based Similarity Features for Efficient Medical Image Retrieval System

by N.Gnaneswara Rao, V Vijaya Kumar, P S V Srinivasa Rao
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 20 - Number 7
Year of Publication: 2011
Authors: N.Gnaneswara Rao, V Vijaya Kumar, P S V Srinivasa Rao
10.5120/2446-3303

N.Gnaneswara Rao, V Vijaya Kumar, P S V Srinivasa Rao . Novel Approaches of Evaluating Texture based Similarity Features for Efficient Medical Image Retrieval System. International Journal of Computer Applications. 20, 7 ( April 2011), 20-26. DOI=10.5120/2446-3303

@article{ 10.5120/2446-3303,
author = { N.Gnaneswara Rao, V Vijaya Kumar, P S V Srinivasa Rao },
title = { Novel Approaches of Evaluating Texture based Similarity Features for Efficient Medical Image Retrieval System },
journal = { International Journal of Computer Applications },
issue_date = { April 2011 },
volume = { 20 },
number = { 7 },
month = { April },
year = { 2011 },
issn = { 0975-8887 },
pages = { 20-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume20/number7/2446-3303/ },
doi = { 10.5120/2446-3303 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:07:09.142074+05:30
%A N.Gnaneswara Rao
%A V Vijaya Kumar
%A P S V Srinivasa Rao
%T Novel Approaches of Evaluating Texture based Similarity Features for Efficient Medical Image Retrieval System
%J International Journal of Computer Applications
%@ 0975-8887
%V 20
%N 7
%P 20-26
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The digital medical images in real world are intensity images that carry less information, more noise, less resolution and poor illumination levels. In such cases the existing retrieval system fails in retrieving the relevant images. This is because the chosen similarity features in those systems are not effective for the above types of image retrieval. To address these issues the present paper proposed new similarity texture feature derived from the novel idea of Basic Texture Unit (BTU), Reduced Texture Unit (RTU) and Fuzzy based Texture Unit (FTU). The Texture Unit (TU) extracts textural information of an image with a more complete respect of texture characteristics in all the eight directions instead of only one displacement vector. In most of the real images two neighboring pixel may not have the same value due to the different processes of capture, illumination levels, poor resolutions or digitations. This criterion is met in the proposed BTS, RTS and FTS derived from BTU, RTU and FTU respectively. The BTU , RTU and FTU gives only ternary ,binary and five values respectively to a texture element and TU ranges 0 to 6561, 0 to 255 and 0 to 2020 respectively. The similarity features are extracted on BTU, RTU and FTU schemes and a good comparison is made. The experimental results on MRI and Orthopedics images indicate reliability, feasibility and efficacy of the proposed methods.

References
  1. R. Haralick, K. Shanmugam, and I. Dinstein. Textural features for image classifcation. IEEE Transactions Systems on Man and Cybernetics, 3(6):610 621,1973.
  2. Amith k Bhattacharya, P K Srivatsava and Anil Bhagat., a modified Texture filter for Satellite images,22nd Asian Conference on Remote Sensing,5-9 November 2001,Singapore.
  3. He, D.C., and L.Wang, 1990. Texture unit, texture spectrum and texture analysis. IEEE Trans. Geoscience Remote Sensing, 28(4), pp.509-512
  4. Wang, L. and He, D.C. Texture Classification Using Texture Spectrum, Pattern Recognition, Vol. 23, pp. 905-910, 1990
  5. Van Gool, L., Dewaele, P. and Oosterlinck, A. Survey-texture analysis anno 1983, Computer Vision, Graphics Image Processing, Vol. 29, PP. 336-357, 1985.
  6. Salari, E. and Ling, Z. Texture segmentation using hierarchical wavelet decomposition, Patt. Recogn. 28, 12, pp.1819-1824, 1995.
  7. Bovik, A. C., Clark, M. and Geisler, W. S. Multichannel texture analysis using localized spatial filters, IEEE Trans. Patt. Anal. Mach. Intell., 12, 1, pp. 55-73, 1990.
  8. Chang. T, and Kuo, C. C. J., Texture analysis and classification with tree-structured wavelet transform, IEEE Trans. Image Processing, 2, 4, pp. 429-442, 1993.
  9. Chen, J. L. and Kundu, Unsupervised texture segmentation using multi-channel decomposition and hidden Markov models, IEEE Trans. Image. Processing, 4, 5, pp. 603-620, 1995.
  10. Haralick, R. M. Statistical and structural approaches to texture, Proc. of 4th Int. Joint Conf. Pattern Recognition, pp. 45-60, 1979.
  11. He, D. C. and Wang, L. Textural filters based on the texture spectrum, Patt. Recogn. 24, 12, pp.1187-1195, 1991.
  12. He, D.C. and Li, Wang. Texture Features Based on Texture Spectrum, Pattern Recognition, Vol. 24, pp. 391-399, 1991.
  13. H.Müller, N.Michous, D.Bandon and A.Geissbuhler. A review of content-based image retrieval systems in medical applications – clinical benefits and future directions. International Journal of Medical Informatics, 73(1):-23,Feb, 2004.
  14. A. Kak and C. Pavlopoulou. Content-Based Image Retrieval from Large Medical Databases. In 3D Data Processing, Visualization, Transmission, Padova, Italy, June 2002.
  15. F. Korn, N. Sidiropoulos, C. Faloustos, E. Siegel, and Z. Protopapas. Fast and effective retrieval of medical tumor shapes. IEEE Transactions on Knowledge and Data Engineering, 10(6):889–904, 1998.
  16. T. Lehmann, B. Wein, J. Dahmen, J. Bredno,F. Vogelsang, and M. Kohnen. Content-Based Image Retrieval in Medical Applications : A Novel Multi-Step Approach. In International Society for Optical Engineering (SPIE), volume 3972(32), pages 312–320, Feb. 2000.
  17. H. D. Tagare, C. C. Jaffe, and J. Duncan. Medical Image Databases: A Content-based Retrieval Approach. J Am Med Inform Assoc, 4(3):184–198,1997.
  18. Tristan Glatard, Johan Montagnat, Isabelle E. Magnin CREATIS (CNRS-Inserm), INSA, Texture Based Medical Image Indexing and Retrieval: Application to Cardiac Imaging, MIR’04, October 15–16, 2004, New York, New York, USA
  19. Vijaya Kumar , N. Gnaneswara Rao, A.L.Narsimha Rao, V.Venkata Krishna, IHBM: Integrated Histogram Bin Matching For Similarity Measures of Color Image Retrieval, International Journal of Signal Processing, Image Processing and Pattern Recognition,IJSIP, Vol. 2, No. 3, September, 2009
  20. M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee,D. Petkovic, D. Steele, and P. Yanker. Query by image and video content : The QBIC system. IEEE Computer, 28(9):23–32, 1995.
  21. Vijaya Kumar , N. Gnaneswara Rao, A.L.Narsimha Rao, RTL: Reduced Texture spectrum with Lag value Based Image Retrieval for Medical Images, International Journal of Future Generation Communication and Networking Vol. 2, No. 4, December, 2009
Index Terms

Computer Science
Information Sciences

Keywords

Medical imaging texture texture spectrum similarity feature image retrieval

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