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Integrating Multiple Feature Descriptors for Computed Tomography Image Retrieval

  • Xiaoqin Wang
  • Huadeng WangEmail author
  • Rushi Lan
  • Xiaonan Luo
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 895)

Abstract

Integrating multiple feature descriptors has recently shown to give excellent results for image retrieval. In this paper, we integrate multiple feature descriptors for computed tomography (CT) image retrieval, whose descriptors include the principal components descriptor, scale invariant feature transform descriptor and roberts gradient descriptor. First, we describe the retrieving image based on principal components descriptor, which is a technology of reducing the dimensions and extracting principal component. Second, we extract the scale invariant feature transform descriptor based on scale invariant feature transform algorithm. Third, the roberts gradient descriptor is obtained by roberts operator. Finally, we integrate principal components descriptor, scale invariant feature transform descriptor and roberts gradient descriptor into a retrieval vector to represent the CT image. Experimental results based on a subset of EXACT09-CT, named CASE23 and TCIA-CT show that our approach significantly outperforms the methods of the related works.

Keywords

CT image retrieval Multiple feature descriptors PCA SIFT Roberts operator 

References

  1. 1.
    Akgül, C.B., Rubin, D.L., Napel, S., Beaulieu, C.F., Greenspan, H., Acar, B.: Content-based image retrieval in radiology: current status and future directions. J. Digit. Imaging 24(2), 208–222 (2011)CrossRefGoogle Scholar
  2. 2.
    Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., et al.: The cancer imaging archive (tcia): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013)CrossRefGoogle Scholar
  3. 3.
    Dubey, S.R., Singh, S.K., Singh, R.K.: Local wavelet pattern: a new feature descriptor for image retrieval in medical CT databases. IEEE Trans. Image Process. 24(12), 5892–5903 (2015)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Giveki, D., Soltanshahi, M.A., Montazer, G.A.: A new image feature descriptor for content based image retrieval using scale invariant feature transform and local derivative pattern. Opt. Int. J. Light Electron. Opt. 131, 242–254 (2017)CrossRefGoogle Scholar
  5. 5.
    Günen, M.A., Atasever, Ü.H., Beşdok, E.: A novel edge detection approach based on backtracking search optimization algorithm (BSA) clustering. In: 2017 8th International Conference on Information Technology (ICIT), pp. 116–122. IEEE (2017)Google Scholar
  6. 6.
    Howarth, P., Yavlinsky, A., Heesch, D., Rüger, S.: Medical image retrieval using texture, locality and colour. In: Workshop of the Cross-Language Evaluation Forum for European Languages, pp. 740–749. Springer (2004)Google Scholar
  7. 7.
    Khatami, A., Khosravi, A., Nguyen, T., Lim, C.P., Nahavandi, S.: Medical image analysis using wavelet transform and deep belief networks. Expert Syst. Appl. 86, 190–198 (2017)CrossRefGoogle Scholar
  8. 8.
    Kitagawa, M., Shimizu, I., Sara, R.: High accuracy local stereo matching using dog scale map. In: 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), pp. 258–261. IEEE (2017)Google Scholar
  9. 9.
    Lakshmi, K.D., Vaithiyanathan, V.: Image registration techniques based on the scale invariant feature transform. IETE Tech. Rev. 34(1), 22–29 (2017)CrossRefGoogle Scholar
  10. 10.
    Lan, R., Zhou, Y.: Quaternion-michelson descriptor for color image classification. IEEE Trans. Image Process. 25(11), 5281–5292 (2016)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Lan, R., Zhou, Y.: Medical image retrieval via histogram of compressed scattering coefficients. IEEE J. Biomed. Health Inform. 21(5), 1338–1346 (2017)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Lan, R., Zhou, Y., Tang, Y.Y.: Quaternionic weber local descriptor of color images. IEEE Trans. Circuits Syst. Video Technol. 27(2), 261–274 (2017)CrossRefGoogle Scholar
  13. 13.
    Li, Z., Zhang, X., Müller, H., Zhang, S.: Large-scale retrieval for medical image analytics: a comprehensive review. Med. Image Anal. 43, 66–84 (2018)CrossRefGoogle Scholar
  14. 14.
    Lu, H., Li, B., Zhu, J., Li, Y., Li, Y., Xu, X., He, L., Li, X., Li, J., Serikawa, S.: Wound intensity correction and segmentation with convolutional neural networks. Concurrency Comput. Pract. Experience 29(6), e3927 (2017)CrossRefGoogle Scholar
  15. 15.
    Memon, M.H., Li, J.-P., Memon, I., Arain, Q.A.: Geo matching regions: multiple regions of interests using content based image retrieval based on relative locations. Multimedia Tools Appl. 76(14), 15 377–15 411 (2017)CrossRefGoogle Scholar
  16. 16.
    Müller, H., Michoux, N., Bandon, D., Geissbuhler, A.: A review of content-based image retrieval systems in medical applicationsclinical benefits and future directions. Int. J. Med. Inform. 73(1), 1–23 (2004)CrossRefGoogle Scholar
  17. 17.
    Murala, S., Wu, Q.J.: Local ternary co-occurrence patterns: a new feature descriptor for MRI and CT image retrieval. Neurocomputing 119, 399–412 (2013)CrossRefGoogle Scholar
  18. 18.
    Murala, S., Wu, Q.J.: Local mesh patterns versus local binary patterns: biomedical image indexing and retrieval. IEEE J. Biomed. Health Inform. 18(3), 929–938 (2014)CrossRefGoogle Scholar
  19. 19.
    Murala, S., Wu, Q.J.: Spherical symmetric 3D local ternary patterns for natural, texture and biomedical image indexing and retrieval. Neurocomputing 149, 1502–1514 (2015)CrossRefGoogle Scholar
  20. 20.
    Naidu, V., Raol, J.R.: Pixel-level image fusion using wavelets and principal component analysis. Defence Sci. J. 58(3), 338 (2008)CrossRefGoogle Scholar
  21. 21.
    Niu, S., Chen, Q., De Sisternes, L., Ji, Z., Zhou, Z., Rubin, D.L.: Robust noise region-based active contour model via local similarity factor for image segmentation. Pattern Recogn. 61, 104–119 (2017)CrossRefGoogle Scholar
  22. 22.
    Ojala, T., Pietikainen, M., 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)CrossRefGoogle Scholar
  23. 23.
    Velmurugan, K.: A survey of content-based image retrieval systems using scale-invariant feature transform (sift). In: International Journal of Advanced Re-search in Computer Science and Software Engineering, vol. 4 (2014)Google Scholar
  24. 24.
    Zhang, G., Ma, Z.-M.: Texture feature extraction and description using gabor wavelet in content-based medical image retrieval. In: International Conference on Wavelet Analysis and Pattern Recognition. ICWAPR 2007, vol. 1, pp. 169–173. IEEE (2007)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Xiaoqin Wang
    • 1
  • Huadeng Wang
    • 1
    Email author
  • Rushi Lan
    • 1
  • Xiaonan Luo
    • 1
  1. 1.Guangxi Key Laboratory of Intelligent Processing of Computer Images and GraphicsGuilin University of Electronic TechnologyGuilinChina

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