A Rigid Image Registration by Combined Local Features and Genetic Algorithms

Meskine, Fatiha; Mezouar, Oussama

doi:10.2478/acss-2023-0025

Abstract

Image registration is an essential pre-processing step required for many image processing applications such as medical imaging and computer vision. The aim is to geometrically align two or more images of the same scene by establishing a mapping that relies on each point from one image to its corresponding point of another image. Scale invariant feature transform (SIFT) and speeded up robust features (SURF) are well-liked local features descriptors that have been extensively utilised for feature-based image registration due to their inherent properties such as invariance, changes in illumination, and noise. Moreover, the task of registration can be viewed as an optimization problem that can be solved by applying genetic algorithms (GAs). This paper presents an efficient feature image registration method based on combined local features and GAs. Firstly, the procedure consists of extracting the local features from the images by combining SIFT and SURF algorithms and matching them to refine the feature set data. Therefore, an adaptive GA based on fitness sharing and elitism techniques is employed to find the optimal rigid transformation parameters that best align the feature points by minimizing a distance metric. The suggested method is applied for registering medical images and the obtained results are significant compared to other feature-based approaches with reasonable computation time.

References

B. Zitova and J. Flusser, “Image registration methods: a survey,” Image and Vision Computing, vol. 21, no. 11, pp. 977–1000, Oct. 2003. https://doi.org/10.1016/S0262-8856(03)00137-9
Search in Google Scholar Back to article
O. Cordón, S. Damas, and J. Santamaría, “A CHC evolutionary algorithm for 3D image registration,” in Lecture Notes in Artificial Intelligence, vol. 2715, Springer Berlin, 2003, pp. 404–411. https://doi.org/10.1007/3-540-44967-1_48
Search in Google Scholar Back to article
S. Y. Guan, T. M. Wang, C. Meng, and J. C. Wang, “A review of point feature based medical image registration,” Chin. J. Mech. Eng., vol. 31, Aug. 2018. https://doi.org/10.1186/s10033-018-0275-9
Search in Google Scholar Back to article
Y. Bentoutou, N. Taleb, K. Kpalma, and J. Ronsin, “An automatic image registration for applications in remote sensing,” IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 9, pp. 2127–2137, Aug. 2005. https://doi.org/10.1109/TGRS.2005.853187
Search in Google Scholar Back to article
F. Meskine, M. Chikr, E. L. Mezouar, and N. Taleb, “A rigid image registration based on the nonsubsampled contourlet transform and genetic algorithms,” Sensors, vol. 10, no. 9, pp. 8553–8571, Sep. 2010. https://doi.org/10.3390/s100908553
Search in Google Scholar Back to article
J. V. Hajnal, D. L. Hill, and D. J. Hawkes, Medical Image Registration. CRC Press, New York, NY, USA, 2001.
Search in Google Scholar Back to article
F. P. Oliveira and J. M. Tavares, “Medical image registration: A review,” Comput. Methods Biomech. Biomed. Engineering, vol. 17, no. 2, pp. 73–93, Mar. 2014. https://doi.org/10.1080/10255842.2012.670855
Search in Google Scholar Back to article
V. R. S. Mani and S. Arivazhagan, “Survey of medical image registration,” Journal of Biomedical Engineering and Technology, vol. 1, no. 2, pp. 8–25, Jan. 2013.
Search in Google Scholar Back to article
G. Song, J. Han, Y. Zhao, Z. Wang, and H. Du, “A review on medical image registration as an optimization problem,” Curr. Med. Imaging Rev., vol. 13, no. 3, pp. 274–283, Aug. 2017. https://doi.org/10.2174/1573405612666160920123955
Search in Google Scholar Back to article
K. Mikolajczyk and C. Schmid, “A performance evaluation of local descriptors,” IEEE Transaction on Pattern Analysis & Machine Intelligence, vol. 27, no. 10, pp. 1615–1630, Aug. 2005. https://doi.org/10.1109/TPAMI.2005.188
Search in Google Scholar Back to article
C. Liu, J. Xu, and F. Wang, “A review of key-points’ detection and feature description in image registration,” Scientific Programming, vol. 2021, 2021, Art. no. 8509164. https://doi.org/10.1155/2021/8509164
Search in Google Scholar Back to article
L. J. Zhi, S. M. Zhang, D. Z. Zhao, H. Zhao, and S. K. Lin, “Medical image retrieval using SIFT feature,” in 2nd International Congress on Image and Signal Processing, Tianjin, China, Oct. 2009. https://doi.org/10.1109/CISP.2009.5304112
Search in Google Scholar Back to article
K. D. Lakshmi and V. Vaithiyanathan, “Image registration techniques based on the scale invariant feature transform,” IETE Technical Review, vol. 34, no. 1, Feb. 2016. https://doi.org/10.1080/02564602.2016.1141076
Search in Google Scholar Back to article
S. Dusty, C. I. Chen, C. M. Tsai, Y. F. Wang, and D. Koppel, “Feature detector and descriptor for medical images,” in Proc. SPIE Medical Imaging: Image Processing, vol. 72592, Lake Buena Vista, Florida, USA, Mar. 2009. https://doi.org/10.1117/12.811210
Search in Google Scholar Back to article
F. Lecron, M. Benjelloun, and S. Mahmoudi, “Descriptive image feature for object detection in medical images,” in Image Analysis and Recognition, ICIAR 2012, Lectures Notes in Computer Science, A. Campilhoand M. Kamel, Eds., vol. 7325, 2012. https://doi.org/10.1007/978-3-642-31298-4_39
Search in Google Scholar Back to article
Y. Lu, K. Gao, T. Zhang, and T. Xu, “A novel image registration approach via combining local features and geometric invariants,” PLoS ONE, vol. 13, no. 1, Jan. 2018, Art. no. e0190383. https://doi.org/10.1371/journal.pone.0190383
Search in Google Scholar Back to article
J. M. Rouet, J. J. Jacq, and C. Roux, “Genetic algorithms for a robust 3-D MR-CT registration,” IEEE Transactions on Information Technology in Biomedicine, vol. 4, no. 2, pp. 126–136, Jun. 2000. https://doi.org/10.1109/4233.845205
Search in Google Scholar Back to article
S. Damas, O. Cordón, and J. Santamaría, “Medical image registration using evolutionary computation : An experimental survey,” IEEE Computational Intelligence Magazine,vol. 6, no. 4, pp. 26–42, Nov. 2011. https://doi.org/10.1109/MCI.2011.942582
Search in Google Scholar Back to article
A. Valsecchi, S. Damas, J. Santamaria, and L. Marrachi-Kacem, “Genetic algorithms for voxel-based medical image registration,” in IEEE International Workshop on Computational Intelligence in Medical Imaging, Singapore, 2013. https://doi.org/10.1109/CIMI.2013.6583853
Search in Google Scholar Back to article
D. G. Lowe, “Distinctive image features from scale-invariant key-points,” Int. Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, Nov. 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
Search in Google Scholar Back to article
M. Kashif, T. M. Deserno, D. Haak, and S. Jonas, “Feature description with SIFT, SURF, BRIEF, BRISK, or FREAK? A general question answered for bone age assessment,” Computers in Biology and Medicine, vol. 68, pp. 67–75, Jan. 2016. https://doi.org/10.1016/j.compbiomed.2015.11.006
Search in Google Scholar Back to article
H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, “Speed up robust features (SURF),” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346–359, Jun. 2008. https://doi.org/10.1016/j.cviu.2007.09.014
Search in Google Scholar Back to article
J. Liu and F. Bu, “Improved RANSAC feature image-matching method based on SURF,” The Journal of Engineering, vol. 2019, no. 23, pp. 9118–9122, Nov. 2019. https://doi.org/10.1049/joe.2018.9198
Search in Google Scholar Back to article
M. P. Deshmukh and U. Bhosle, “A survey of image registration,” Int. Journal of Image Processing, vol. 5, no. 3, pp. 245–269, 2011. https://www.cscjournals.org/manuscript/Journals/IJIP/Volume5/Issue3/IJIP-364.pdf
Search in Google Scholar Back to article
K. F. Man, K. S. Tang, and S. Kwong, “Genetic algorithms: Concept and applications,” IEEE Transactions on Industrial Electronics, vol. 43, no. 5, pp. 519–534, Oct. 1996. https://doi.org/10.1109/41.538609
Search in Google Scholar Back to article
R. Gàbor and A. Ekart, “Genetic algorithms in computer-aided design,” Computer Aided Design, vol. 35, no. 8, pp. 709–726, Jul. 2003. https://doi.org/10.1016/S0010-4485(03)00003-4
Search in Google Scholar Back to article
P. G. Pascale and T. L. Troiano, “A niche based genetic algorithm for image registration,” in Proceedings of the Ninth International Conference on Entreprise Information Systems, Funchal, Madeira, Portugal, Jun. 2007, pp. 342–347. https://www.researchgate.net/publication/220711762_A_Niche_Based_Genetic_Algorithm_for_Image_Registration
Search in Google Scholar Back to article
A. Gasparri, S. Panzieri, and A. Priolo, “A fitness-sharing based genetic algorithm for collaborative multi-robot localization,” Intelligent Service Robotics, vol. 3, pp. 137–149, May 2010. https://doi.org/10.1007/s11370-010-0065-4
Search in Google Scholar Back to article

A Rigid Image Registration by Combined Local Features and Genetic Algorithms

Abstract

Paradigm

My account