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A Robust and Invariant Keypoint Extraction Algorithm in Brain MR Images

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Artificial Intelligence and Signal Processing (AISP 2013)

Abstract

In this paper a method for extracting keypoints from human brain MR images is proposed. These keypoints are obtained based on curved structures in the brain MR images. In this method, a keypoint is center of a circle which includes circular boundaries in the image and is selected based on gradients of the image. These keypoints and their descriptors are scale and rotation invariant. The proposed method is compared with other well-known methods with repeatability measure and ROC curves. Experimental results show that proposed method performs better than other well-known methods, specially, when deformations are remarkable.

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References

  1. Lin, H., Du, P., Zhao, W., Zhang, L., Sun, H.: Image registration based on corner detection and affine transformation. In: 3rd International Congress Image and Signal Processing (CISP), pp. 2184–218 (2010)

    Google Scholar 

  2. Pei, Y., Wu, H., Yu, J., Cai, G.: Effective image registration based on improved harris corner detection. In: International Conference on Information Networking and Automation (ICINA), pp. V1-93–V96-1 (2010)

    Google Scholar 

  3. Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, pp. 147–151 (1988)

    Google Scholar 

  4. Davatzikos, C., Prince, J.L., Bryan, R.N.: Image registration based on boundary mapping. IEEE Trans. Med. Imaging 15, 112–115 (1996)

    Article  Google Scholar 

  5. Vasileisky, A., Zhukov, B., Berger, M.: Automated image coregistration based on linear feature recognition. In: Proceedings of the Second Conference Fusion of Earth Data, Sophia Antipolis, France, pp. 59–66 (1998)

    Google Scholar 

  6. Rosten, E., Drummond, T.W.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  7. Rosten, E., Porter, R., Drummond, T.: Faster and better: a machine learning approach to corner detection. IEEE Trans. Pattern Anal. Mach. Intell. 32, 105–119 (2010)

    Article  Google Scholar 

  8. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91–110 (2004)

    Article  Google Scholar 

  9. Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, pp. 1150–1157 (1999)

    Google Scholar 

  10. Alhichri, H.S., Kamel, M.: Virtual circles: a new set of features for fast image registration. Pattern Recogn. Lett. 24, 1181–1190 (2003)

    Article  MATH  Google Scholar 

  11. Borgefors, G.: Distance transformations in digital images. Comput. Vision Graph. Image Proc. 34, 344–371 (1986)

    Article  Google Scholar 

  12. Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Proc. 19, 1657–1663 (2010)

    Article  Google Scholar 

  13. Bookstein, F.L.: Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans. Pattern Anal. Mach. Intell 6, 567–585 (1989)

    Article  Google Scholar 

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Correspondence to Hossein Sarikhani .

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Sarikhani, H., Abdollahian, E., Shirpour, M., Javaheri, A., Manzuri, M.T. (2014). A Robust and Invariant Keypoint Extraction Algorithm in Brain MR Images. In: Movaghar, A., Jamzad, M., Asadi, H. (eds) Artificial Intelligence and Signal Processing. AISP 2013. Communications in Computer and Information Science, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-319-10849-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-10849-0_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10848-3

  • Online ISBN: 978-3-319-10849-0

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