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Reseach Article

Spine MRI Image Retrieval using Texture Features

by N. Kumaran, R. Bhavani
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 46 - Number 24
Year of Publication: 2012
Authors: N. Kumaran, R. Bhavani
10.5120/7120-9178

N. Kumaran, R. Bhavani . Spine MRI Image Retrieval using Texture Features. International Journal of Computer Applications. 46, 24 ( May 2012), 1-7. DOI=10.5120/7120-9178

@article{ 10.5120/7120-9178,
author = { N. Kumaran, R. Bhavani },
title = { Spine MRI Image Retrieval using Texture Features },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 46 },
number = { 24 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume46/number24/7120-9178/ },
doi = { 10.5120/7120-9178 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:40:28.046390+05:30
%A N. Kumaran
%A R. Bhavani
%T Spine MRI Image Retrieval using Texture Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 46
%N 24
%P 1-7
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The main intention of content based medical image retrieval (CBMIR) is to efficiently retrieve medical images that are visually similar to a query image. Medical images are usually retrieved on the basis of low level and high level features. This work deals with the concept of texture based spine MRI image retrieval in the wavelet compressed domain. We use two statistical methods such as Haralick features and texture??spectrum features for spine MRI image feature extraction and project the features to a set of signatures. The obtained statistical features are classifying, according to various types of spine MRI images using k-means clustering algorithm. Then the image retrieval is carried out by calculating the distance between the signatures in the database images and the query image. This method is applied around 500 spine MRI images and improvements of retrieval efficiency are found with standard precision and recall analysis.

References
  1. A. W. M. Smeulders, et al. , "Content-based image retrieval at the end of the early years", IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 22, pp. 1349–1379, Dec 2000.
  2. M. Babu Rao, Dr. B. Prabhakara Rao, Dr. A. Govardhan "Content Based Image Retrieval using Dominant Colour, Texture and Shape", International Journal of Engineering Science and Technology, Vol. 3 No. 4, pp. 2887–2896, Apr 2011.
  3. Thomas M. Lehmann, Mark O. Güld, Daniel Keysers, Thomas Deselaers, Henning Schubert, Berthold B. Wein, Klaus Spitzer, "Similarity of Medical Images Computed from Global Feature Vectors for Content-Based Retrieval", In the Proceedings of KES '2004, Vol. 3214, pp. 989–995, Springer-Verlag, Berlin, Heidelberg, 2004.
  4. H. Muller, N. Michoux, D. Bandon, A. Geissbuhler. A review of content-based image retrieval systems in medical applications-clinical bene?ts and future directions. International Journal of Medical Informatics, Vol. 73(1), pp. 1–23, 2004.
  5. X. S. Zhou, S. Zillner, M. Moeller,M. Sintek, Y. Zhan, A. Krishnan, A. Gupta "Semantics and CBIR: A Medical Imaging Perspective", In Proceedings of the ACM International Conference on Content-based Image and Video Retrieval, pp. 571-580, July 2008.
  6. Tristan Glatard, Johan Montagnat, Isabelle E. Magnin "Texture based medical image indexing and retrieval: application to cardiac imaging", In Proceedings of the 6th ACM SIGMM international workshop on "Multimedia Information Retrieval (MIR)", pp. 135—142, October 2004.
  7. V. Vijaya Kumar, N. Gnaneswara Rao, and 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, pp. 39-48, December 2009.
  8. Shao-Hu Peng, Deok-HwanKim, Seok-LyongLee, Myung-KwanLim "Texture feature extraction based on a uniformity estimation method for local brightness and structure in chest CT images", Computers in Biology and Medicine, Vol. 40, pp. 931–942, Elsevier Ltd. 2010.
  9. Yuehua Wan, Shiming Ji, Qiaoling Yuan, Yi Xie, "Multivariate Statistical Modeling for Medical Image Compression Using Wavelet Transforms", In Proceedings 5th IEEE International Conference on Cognitive Informatics, Vol. 1, pp. 13-16, 2006.
  10. G. Quellec, M. Lamard, G. Cazuguel, B. Cochener, C. Roux "Wavelet optimization for content-based image retrieval in medical databases" Medical Image Analysis, Volume 14, No 1, pp. 227–241, Elsevier B. V. , 2009.
  11. A. Mojsilovc and J. Ciomes, "Semantic based categorization, browsing and retrieval in medical image databases", IEEE Proceedings of the International Conference on Image Processing, Vol. 3, pp. 145-148, Sept. 2002.
  12. Lehmann TM, Fischrr B. Giild MO. Thies C. Keysers D, Deselaen T, Schuben H. Weun EB. Spitzer K. " The IRMA Reference Database and Its Use for Content-Based Image Retrieval in Medical Applications", In: Ammenwerth E, Gaus W, Haux R, Lovis C, Pfeiffer KP, Tilg B, Wichmann HE (eds): GMDS 2004 - Supply Cooperative - Virtual Research - Ubiquitous Information. Verlag videel OHC, Niebiill, pp. 251-253, 2004.
  13. A. Grace Selvarani, Dr. S. Annadurai, "Medical Image Retrieval By Combining Low Level Features and Dicom Features ", IEEE International Conference on Computational Intelligent and Multimedia Applications, Vol. 1, pp. 587 - 589 , Dec. 2007.
  14. Gang Zhang, Zong-Min Ma," Texture Feature Extraction and Description Using Gabor Wavelet in Content-Based Medical Image Retrieval ", Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition, Vol. . 1, pp. 2-4, Nov. 2007.
  15. Hayit Greenspan, AdiT. Pinhas, "Medical Image Categorization and Retrieval for PACS Using the GMM-KL Framework", IEEE Transactions on Information Technology in Biomedicine, Vol. 11, No. 2, pp. 190 – 202, March 2007.
  16. Dah-Jye Lee, Sameer Antani, Yuchou Chang, Kent Gledhill, L. Rodney Long, Paul Christensen, "CBIR of spine X-ray images on inter-vertebral disc space and shape pro?les using feature ranking and voting consensus", Data &Knowledge Engineering Vol. 68, pp. 1359–1369, Elsevier B. V. , 2009.
  17. H. Mller, N. Michoux, D. Bandon, and A. Geissbuhler, "A review of content-based image retrieval systems in medical applications-clinical bene?ts and future directions," International Journal of Medical Informatics vol. 73, pp. 1–23, February 2004.
  18. K. Rajakumar and Dr. S. Muttan, "Medical image retrieval using modified DCT", Proceedings of the International Conference and Exhibition on Biometrics Technology, Volume 2, pp. 298-302, Elsevier Ltd. , 2010.
  19. Piotr Porwik, and Agnieszka Lisowska "The Haar-Wavelet Transform in Digital Image Processing: Its Status and Achievements", Machine Graphics & Vision, vol. 13, no. ½, pp. 79-98, 2004.
  20. Junguk Baek, Sangwook Shin, Minhyuk Chang and Jongan Park, "Classification of Feature Set Using K-means Clustering from Histogram Refinement Method" ", Proceedings of the Fourth International Conference on Networked Computing and Advanced Information Management, vol. 2, pp. 320-324, IEEE, Sep. 2008.
  21. Mousa Al-Akhras, Mohammed Alawairdhi and Dana Beidas, "Search in Multimedia Databases Using Similarity Distance", International Journal of Intelligent Information Processing, Vol. 1, No. 1, pp. 50-60, Sep. 2010.
  22. Michael Lam,Tim Disney, Mailan Pham, Daniela Raicu, Jacob Furst, Ruchaneewan Susomboon "Content-Based Image Retrieval for Pulmonary Computed Tomography Nodule Images" Medical Imaging 2007: PACS and Imaging Informatics. Edited by Horii, Steven C. Andriole, Katherine P. Proceedings of the SPIE, Volume 6516, pp. 65160N, 2007.
  23. He and Li Wang, "Texture unit, Texture spectrum and texture analysis", IEEE Transaction on Geo Science and Remote sensing, Vol. 28(4), pp. 509-512, 1990.
  24. D. He and L. Wang, "Texture features based on texture spectrum" Pattern Recognition, Vol. 24(5), pp. 391–399, Elsevier Ltd. , 1991.
Index Terms

Computer Science
Information Sciences

Keywords

Haralick Features Texture Spectrum Features Haar Dwt K-means Clustering