Clustered Redundant Keypoint Elimination SURF method in MRI Image Registration based on Alpha-Trimmed Relationship
Abstract
The process of MRI image registration is one of the important branches in MRI image analysis, which is a necessary preprocessing to the use of information in these images. The Clustered Adaptive Keypoint Elimination method-SIFT (CRKEM-SIFT) algorithm has recently been introduced to eliminate redundancies and upgrade the precision corresponding. The disadvantages of this algorithm are the high execution time and the number of incorrect correspondences. In this paper, to increase the accuracy and speed of MRI image registration, first, the CRKEM method is used on the SURF algorithm. Then, Spatial Relations Correspondence (SRC) and Alpha-Trimmed Spatial Relations Correspondence (ATSRC) methods are suggested to improve correspondences. These suggested methods, unlike conventional methods such as RANSAC, which only eliminates incorrect correspondences, in these suggested methods based on spatial relationships, detect incorrect correspondences and turn them into correct correspondences. Converting incorrect correspondences to correct ones can increase the number of correct correspondences and ultimately increase the precision correspondences. The simulation results confirm the suggested approaches' superiority on standard brain databases compared to classic methods in terms of Maximum error (MAE) and precision.
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[13] Z. Hossein-Nejad and M. Nasri, "RKEM: Redundant Keypoint Elimination Method in Image Registration," IET Image Processing, vol. 11, pp. 273-284, 2017.
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[15] A. Baghaie and Z. Yu, "Curvature-based registration for slice interpolation of medical images," in International Symposium Computational Modeling of Objects Represented in Images, 2014, pp. 69-80.
[16] Z. Ghassabi, J. Shanbehzadeh, A. Sedaghat, and E. Fatemizadeh, "An efficient approach for robust multimodal retinal image registration based on UR-SIFT features and PIIFD descriptors," EURASIP Journal on Image and Video Processing, vol. 2013, pp. 1-16, 2013.
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[18] Z. Hossein-Nejad and M. Nasri, "A Review on Image Registration Methods, Concepts and applications," Journal of Machine Vision and Image Processing, pp. 39-67, 2017.
[19] T. Chen and J. Zhuang, "Performance Evaluation of the Scale-Invariant Feature Transform in Different Modalities of Medical Image Registration," Frontiers in Computer Technology and Applications, vol. 1, pp. 28-32, 2020.
[20] P. Lukashevich, B. Zalesky, and S. Ablameyko, "Medical image registration based on surf detector," Pattern Recognition and Image Analysis, vol. 21, pp. 519-521, 2011.
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[24] Z. Hossein-Nejad and M. Nasri, "Retinal Image Registration based on Auto-Adaptive SIFT and Redundant Keypoint Elimination Method," in 2019 27th Iranian Conference on Electrical Engineering (ICEE), 2019, pp. 1294-1297.
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[26] F. Guo, X. Zhao, B. Zou, and Y. Liang, "Automatic retinal image registration using blood vessel segmentation and SIFT feature," International Journal of Pattern Recognition and Artificial Intelligence, vol. 31, p. 1757006, 2017.
[27] Z. Hossein-Nejad and M. Nasri, "Clustered redundant keypoint elimination method for image mosaicing using a new Gaussian-weighted blending algorithm," The Visual Computer, pp. 1-17, 2021.
[28] W. Aguilar, Y. Frauel, F. Escolano, M. E. Martinez-Perez, A. Espinosa-Romero, and M. A. Lozano, "A robust graph transformation matching for non-rigid registration," Image Vision Computing, vol. 27, pp. 897-910, 2009.
[29] M. A. Fischler and R. C. Bolles, "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography," Communications of the ACM, vol. 24, pp. 381-395, 1981.
[30] Z. Hossein-Nejad and M. Nasri, "An adaptive image registration method based on SIFT features and RANSAC transform," Computers & Electrical Engineering, vol. 62, pp. 524-537, 2017.
[31] Z. Hossein-nejad and m. Nasri, "Adaptive Stopping Criteria-based A-RANSAC algorithm in Copy Move Image Forgery detection," presented at the 12th International Conference on Information and Knowledge Technology (IKT2021), Mazandaran University of Science and Technology, Mazandaran, Iran, 2021.
[32] D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International journal of computer vision, vol. 60, pp. 91-110, 2004.
[33] A. Sedaghat and N. Mohammadi, "High-resolution image registration based on improved SURF detector and localized GTM," International Journal of Remote Sensing, vol. 40, pp. 2576-2601, 2019.
[34] N. I. o. Health, "Retrospective Image Registration Evaluation," Vanderbilt University, Nashville (TN), USA, 2003.
[35] Z. Hossein-nejad and M. Nasri, "Image Registration Based on Redundant Keypoint Elimination SARSIFT Algorithm and MROGH Descriptor," presented at the The 12th Iranian and the second International Conference on Machine Vision and Image Processing, Shahid Chamran University of Ahvaz, Ahvaz, Iran, 2022.
[2] M. Velázquez-Durán, D. Campos-Delgado, E. Arce-Santana, and A. Mejía-Rodríguez, "Multimodal 3D rigid image registration based on expectation maximization," Health and Technology, vol. 10, pp. 429-435, 2020.
[3] L. Cao, H. Li, and Y. Zhang, "Retinal image enhancement using low-pass filtering and α-rooting," Signal Processing, vol. 170, p. 107445, 2020.
[4] Z. Hossein-Nejad and M. Nasri, "Natural Image Mosaicing based on Redundant Keypoint Elimination Method in SIFT algorithm and Adaptive RANSAC method," signal and data processing Journal, Accepted for Publication.
[5] Z. Yang, N. Kuang, Y. Yang, and B. Kang, "Brain MR Multimodal Medical Image Registration Based on Image Segmentation and Symmetric Self-similarity," KSII Transactions on Internet & Information Systems, vol. 14, 2020.
[6] L. Shen, X. Huang, C. Fan, and Y. Li, "Enhanced mutual information-based medical image registration using a hybrid optimisation technique," Electronics Letters, vol. 54, pp. 926-928, 2018.
[7] D. Sengupta, P. Gupta, and A. Biswas, "A Survey on Mutual Information Based Medical Image Registration Algorithms," Neurocomputing, 2021.
[8] Q. Zheng, Q. Wang, X. Ba, S. Liu, J. Nan, and S. Zhang, "A Medical Image Registration Method Based on Progressive Images," Computational and Mathematical Methods in Medicine, vol. 2021, 2021.
[9] R. Ramli, K. Hasikin, M. Y. I. Idris, N. K. A. Karim, and A. W. A. Wahab, "Fundus Image Registration Technique Based on Local Feature of Retinal Vessels," Applied Sciences, vol. 11, p. 11201, 2021.
[10] D. Mahapatra, B. Antony, S. Sedai, and R. Garnavi, "Deformable medical image registration using generative adversarial networks," in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018, pp. 1449-1453.
[11] T. Yang, Q. Tang, L. Li, and X. Bai, "Non-rigid medical image registration using multi-scale residual deep fully convolutional networks," Journal of Instrumentation, vol. 16, p. P03005, 2021.
[12] Y. Chen, X. Zhang, Y. Zhang, S. J. Maybank, and Z. Fu, "Visible and infrared image registration based on region features and edginess," Machine Vision and Applications, vol. 29, pp. 113-123, 2018.
[13] Z. Hossein-Nejad and M. Nasri, "RKEM: Redundant Keypoint Elimination Method in Image Registration," IET Image Processing, vol. 11, pp. 273-284, 2017.
[14] R. François, R. Fablet, and C. Barillot, "Robust statistical registration of 3D ultrasound images using texture information," in Proceedings 2003 international conference on image processing (Cat. No. 03CH37429), 2003, pp. I-581.
[15] A. Baghaie and Z. Yu, "Curvature-based registration for slice interpolation of medical images," in International Symposium Computational Modeling of Objects Represented in Images, 2014, pp. 69-80.
[16] Z. Ghassabi, J. Shanbehzadeh, A. Sedaghat, and E. Fatemizadeh, "An efficient approach for robust multimodal retinal image registration based on UR-SIFT features and PIIFD descriptors," EURASIP Journal on Image and Video Processing, vol. 2013, pp. 1-16, 2013.
[17] Z. Hossein-nejad and M. Nasri, "Image registration based on SIFT features and adaptive RANSAC transform," in Communication and Signal Processing (ICCSP), 2016 International Conference on, 2016, pp. 1087-1091.
[18] Z. Hossein-Nejad and M. Nasri, "A Review on Image Registration Methods, Concepts and applications," Journal of Machine Vision and Image Processing, pp. 39-67, 2017.
[19] T. Chen and J. Zhuang, "Performance Evaluation of the Scale-Invariant Feature Transform in Different Modalities of Medical Image Registration," Frontiers in Computer Technology and Applications, vol. 1, pp. 28-32, 2020.
[20] P. Lukashevich, B. Zalesky, and S. Ablameyko, "Medical image registration based on surf detector," Pattern Recognition and Image Analysis, vol. 21, pp. 519-521, 2011.
[21] B. Rister, M. A. Horowitz, and D. L. Rubin, "Volumetric image registration from invariant keypoints," IEEE Transactions on Image Processing, vol. 26, pp. 4900-4910, 2017.
[22] W. Cao, F. Lyu, Z. He, G. Cao, and Z. He, "Multimodal medical image registration based on feature spheres in geometric algebra," IEEE Access, vol. 6, pp. 21164-21172, 2018.
[23] A. Liu, "Eyeball Image Registration and Fusion Based on SIFT+ RANSAC Algorithm," Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology: Algorithms and Applications, Proceedings of IC3DIT 2019, Volume 2, vol. 180, p. 223, 2020.
[24] Z. Hossein-Nejad and M. Nasri, "Retinal Image Registration based on Auto-Adaptive SIFT and Redundant Keypoint Elimination Method," in 2019 27th Iranian Conference on Electrical Engineering (ICEE), 2019, pp. 1294-1297.
[25] Z. Gu, L. Cai, Y. Yin, Y. Ding, and H. Kan, "Registration of brain medical images based on surf algorithm and r-ransac algorithm," Telkomnika Indonesian Journal of Electrical Engineering, vol. 12, pp. 2290-2297, 2014.
[26] F. Guo, X. Zhao, B. Zou, and Y. Liang, "Automatic retinal image registration using blood vessel segmentation and SIFT feature," International Journal of Pattern Recognition and Artificial Intelligence, vol. 31, p. 1757006, 2017.
[27] Z. Hossein-Nejad and M. Nasri, "Clustered redundant keypoint elimination method for image mosaicing using a new Gaussian-weighted blending algorithm," The Visual Computer, pp. 1-17, 2021.
[28] W. Aguilar, Y. Frauel, F. Escolano, M. E. Martinez-Perez, A. Espinosa-Romero, and M. A. Lozano, "A robust graph transformation matching for non-rigid registration," Image Vision Computing, vol. 27, pp. 897-910, 2009.
[29] M. A. Fischler and R. C. Bolles, "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography," Communications of the ACM, vol. 24, pp. 381-395, 1981.
[30] Z. Hossein-Nejad and M. Nasri, "An adaptive image registration method based on SIFT features and RANSAC transform," Computers & Electrical Engineering, vol. 62, pp. 524-537, 2017.
[31] Z. Hossein-nejad and m. Nasri, "Adaptive Stopping Criteria-based A-RANSAC algorithm in Copy Move Image Forgery detection," presented at the 12th International Conference on Information and Knowledge Technology (IKT2021), Mazandaran University of Science and Technology, Mazandaran, Iran, 2021.
[32] D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International journal of computer vision, vol. 60, pp. 91-110, 2004.
[33] A. Sedaghat and N. Mohammadi, "High-resolution image registration based on improved SURF detector and localized GTM," International Journal of Remote Sensing, vol. 40, pp. 2576-2601, 2019.
[34] N. I. o. Health, "Retrospective Image Registration Evaluation," Vanderbilt University, Nashville (TN), USA, 2003.
[35] Z. Hossein-nejad and M. Nasri, "Image Registration Based on Redundant Keypoint Elimination SARSIFT Algorithm and MROGH Descriptor," presented at the The 12th Iranian and the second International Conference on Machine Vision and Image Processing, Shahid Chamran University of Ahvaz, Ahvaz, Iran, 2022.
| Files | ||
| Issue | Vol 10 No 2 (2023) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v10i2.12216 | |
| Keywords | ||
| brain image registration CRKEM-SIFT spatial relations redundant keypoints MRI | ||
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How to Cite
1.
Hossein-Nejad Z, Nasri M. Clustered Redundant Keypoint Elimination SURF method in MRI Image Registration based on Alpha-Trimmed Relationship. Frontiers Biomed Technol. 2023;10(2):120-131.
