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Rat Brain Registration Using Improved Speeded Up Robust Features

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Abstract

This study proposes an approach that combines edge feature extraction (EFE) with speeded up robust features (SURF) to register rat brain slices. When extracting feature points, using the traditional scale-invariant feature transform (SIFT) approach has several shortcomings, including blurry edge information and the inability to accurately extract smooth edge targets. Therefore, a feature point description method that comprises EFE and SURF is proposed to match local feature points, thereby effectively capturing edge feature points and avoiding mistaking grains and noise in images for feature points. In addition, a wavelet feature description approach is used to resolve incorrect matching caused by noise. Feature point matching for local regions is devised to solve mismatch errors that occur in highly similar regions in an image. Compared to a state-of-the-art SIFT method, the proposed method generates better results for rat brain image registration in terms of subjective visual presentation and registration quality criteria.

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References

  1. Sdika, M. (2008). A fast nonrigid image registration with constraints on the jacobian using large scale constrained optimization. IEEE Transactions on Medical Imaging, 27, 271–281.

    Article  Google Scholar 

  2. Hsu, W. Y. (2015). A novel image registration algorithm for indoor and built environment applications. Computer-Aided Civil and Infrastructure Engineering, 30, 802–814.

    Article  Google Scholar 

  3. Wittek, A., Miller, K., Kikinis, R., & Warfield, S. (2007). Patient-specific model of brain deformation: Application to medical image registration. Journal of Biomechanics, 40, 919–929.

    Article  Google Scholar 

  4. Shen, D. (2007). Image registration by local histogram matching. Pattern Recognition, 40, 1161–1172.

    Article  Google Scholar 

  5. Hsu, W. Y. (2012). Registration accuracy and quality of real-life images. PLoS ONE, 7, e40558.

    Article  Google Scholar 

  6. Holden, M. (2008). A review of geometric transformations for nonrigid body registration. IEEE Transactions on Medical Imaging, 27, 111–128.

    Article  Google Scholar 

  7. Hsu, W. Y., & Chen, K. W. (2014). Segmentation-based image compression using modified competitive network. Journal of Medical and Biological Engineering, 34, 542–546.

    Article  Google Scholar 

  8. D’Agostino, E., Maes, F., Vandermeulen, D., & Suetens, P. (2006). An information theoretic approach for non-rigid image registration using voxel class probabilities. Medical Image Analysis, 10, 413–431.

    Article  Google Scholar 

  9. Hsu, W. Y. (2011). Analytic differential approach for robust registration of rat brain histological images. Microscopy Research and Technique, 74, 523–530.

    Article  Google Scholar 

  10. Simonetti, A. W., Elezi, V. A., Farion, R., Malandain, G., Segebartha, C., Remy, C., et al. (2006). A low temperature embedding and section registration strategy for 3D image reconstruction of the rat brain from autoradiographic sections”. Journal of Neuroscience Methods, 158, 242–250.

    Article  Google Scholar 

  11. Hsu, W. Y., & Chou, C. Y. (2015). Medical image enhancement using modified color histogram equalization. Journal of Medical and Biological Engineering, 35, 580–584.

    Article  Google Scholar 

  12. Periaswamy, S., & Farid, H. (2006). Medical image registration with partial data. Medical Image Analysis, 10, 452–464.

    Article  Google Scholar 

  13. Hsu, W. Y. (2013). A practical approach based on analytic deformable algorithm for scenic image registration. PLoS ONE, 8, e66656.

    Article  Google Scholar 

  14. Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, 679–698.

    Article  Google Scholar 

  15. Lowe, D. G. (2004). Distinctive image features from scale invariant keypoints. International Journal of Computer Vision, 60, 91–110.

    Article  Google Scholar 

  16. Mikolajczyk, K., & Schmid, C. (2005). A performance evaluation of local descriptors. IEEE Transaction on Pattern Analysis and Machine Intelligence, 27, 1615–1630.

    Article  Google Scholar 

  17. Yan, K. & Sukthankar, R. (2004). PCA-SIFT: A more distinctive representation for local image descriptors. In Proceedinds of 2004 IEEE Conference on computer vision and patten recognition, Vol 2, pp. II506–II513.

  18. Bay, H., Tuytelaars, T. & Gool, L. V. (2006). Surf: Speeded up robust features. Computer vision–ECCV 2006, (pp. 404–417).

    Chapter  Google Scholar 

  19. Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24, 381–395.

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The author would like to express his sincere appreciation for the partial Grants from the Ministry of Science and Technology, Taiwan (MOST103-2410-H-194-070-MY2 and MOST105-2410-H-194-059-MY3).

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Authors and Affiliations

  1. Department of Information Management and Advanced Institute of Manufacturing with High-tech Innovations, National Chung Cheng University, 168 University Rd. Sec. 1, Minhsiung Township, Chiayi County, 621, Taiwan

    Wei-Yen Hsu & Yan-Chieh Lee

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  1. Wei-Yen Hsu
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  2. Yan-Chieh Lee
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Correspondence to Wei-Yen Hsu.

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Hsu, WY., Lee, YC. Rat Brain Registration Using Improved Speeded Up Robust Features. J. Med. Biol. Eng. 37, 45–52 (2017). https://doi.org/10.1007/s40846-016-0204-2

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  • DOI: https://doi.org/10.1007/s40846-016-0204-2

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