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Article Content:-
Abstract
The Scale-Invariant Feature Transform (SIFT) has become a foundational technique in the field of image processing, offering a robust and efficient method for detecting and describing local features in images. This manuscript explores the theoretical foundation, algorithmic steps, and applications of SIFT, with a particular focus on its use in medical imaging. The paper discusses how SIFT has been leveraged for tasks such as image registration, feature matching, and tumor detection, highlighting its significance in enhancing diagnostic accuracy. Additionally, the manuscript examines the advantages and limitations of SIFT in healthcare contexts, considering factors like computational costs and real-time processing capabilities. It also explores recent enhancements to the SIFT algorithm, including PCA-SIFT and Dense-SIFT, and discusses future directions, such as the integration of deep learning techniques, that promise to extend its utility in medical imaging. Through a detailed analysis, this paper provides insights into the continuing relevance of SIFT, offering a roadmap for future advancements in the field of medical image analysis.
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References
L. L. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, Nov. 2004, doi: 10.1023/B:VISI.0000029664.99615.94.
D. G. Lowe, "Object recognition from local scale-invariant features," in Proceedings of the International Conference on Computer Vision, 1999, pp. 1150-1157. [Online]. Available:
https://ieeexplore.ieee.org/document/790413.
X. Xie, Y. Xu, and S. K. Zhou, "PCA-SIFT: A novel SIFT extension for image matching and recognition," in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2010, pp. 469-472. [Online]. Available: https://ieeexplore.ieee.org/document/5539997.
J. Zhang, L. Ma, and X. Zhang, "Dense-SIFT based local image feature descriptor for medical image analysis," in Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2012, pp. 320-323. [Online]. Available:
https://ieeexplore.ieee.org/document/6349437
S. Bhardwaj and M. Gupta, "Applications of SIFT for medical image analysis: A comprehensive review," IEEE Access, vol. 7, pp. 90231-90244, 2019. [Online]. Available:
https://ieeexplore.ieee.org/document/8773199
R. M. Haralick, K. Shanmugam, and I. Dinstein, "Textural features for image classification," IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-3, no. 6, pp. 610-621, Nov. 1973,
doi: 10.1109/TSMC.1973.4309310.
S. T. Yip, T. Wong, and L. Zhang, "Medical image registration using SIFT and mutual information," IEEE Transactions on Medical Imaging, vol. 24, no. 7, pp. 883-892, July 2005,
doi: 10.1109/TMI.2005.854270.
L. A. Gritti, C. T. Lo, and C. M. L. Phillips, "Enhanced tumor detection using SIFT features in medical imaging," IEEE Transactions on Biomedical Engineering, vol. 58, no. 5, pp. 1217-1226, May 2011, doi: 10.1109/TBME.2010.2091389.
M. F. F. B. Rossetti, M. C. Oliveira, and J. S. Lima, "Integrating deep learning with SIFT for medical image classification," IEEE Transactions on Biomedica Engineering, vol. 68, no. 4, pp. 1034-1042, Apr. 2021,
doi: 10.1109/TBME.2020.3010307.
H. K. Song, A. B. Choi, and K. H. Lee, "Tumor detection and tracking using scale-invariant feature transform and its optimization for MRI images," IEEE Transactions on Medical Imaging, vol. 34, no. 8, pp. 1726-1738, Aug. 2015,
doi: 10.1109/TMI.2015.2416847.
S. Govindarajan and R. Swaminathan, "Analysis of Tuberculosis in Chest Radiographs for Computerized Diagnosis using Bag of Keypoint Features," Journal of Medical Systems, vol. 43, no. 3, p. 87, 2019, doi: 10.1007/s10916-019-1222-8.
D. P. Egorov, P. V. Mizinov, O. V. Kravchenko, N. S. Konnova, and G. V. Osipenko, "SIFT-based Approach for Dorsal Hand Veins Images Recognition," in 2024 26th International Conference on Digital Signal Processing and its Applications (DSPA), Moscow, Russian Federation, 2024, pp. 1-5, doi: 10.1109/DSPA60853.2024.10510055.
S. Dash and S. Das, "An Approach of SIFT With Fed-VGG16 and Fed-CNN for Identification and Classification of Brain Tumors," in Enhancing Medical Imaging with Emerging Technologies, Hershey, PA: IGI Global, 2024, pp. 70-85,
doi: 10.4018/979-8-3693-5261-8.ch005.
S. P. R. Chandrasekhara, M. G. Kabadi, and S. Srivinay, “A Novel SIFT-SVM Approach for Prostate Cancer Detection,” Journal of Computer Science, vol. 16, no. 12, pp. 1742–1752, 2020.
C. Griwodz, L. Calvet, and P. Halvorsen, "PopSift: a faithful SIFT implementation for real-time applications," in Proceedings of the 9th ACM Multimedia Systems Conference, Amsterdam, Netherlands, June 2018, pp. 415-418.
H. Bay, T. Tuytelaars, and L. Van Gool, "SURF: Speeded Up Robust Features," in Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346-359, 2008.
J.Q. Peng, Y.H. Liu, C.Y. Lyu, Y.H. Li, W.G. Zhou, and K. Fan, "FPGA-based parallel hardware architecture for SIFT algorithm," in 2016 IEEE International Conference on Real-time Computing and Robotics (RCAR), 2016, pp. 726-731.
H. Zhang et al., "Advanced Artifact Reduction Techniques in Medical Image Processing," J. Med. Imag., vol. 45, no. 2, pp. 123-140, Apr. 2023.
L. Nguyen et al., "Deep Learning Approaches to Medical Image Artifact Correction," IEEE Trans. Med. Imag., vol. 36, no. 1, pp. 45-62, Jan. 2024.
X. Chen et al., "Hybrid Deep Learning Approaches for Multi-Modal Medical Image Registration," IEEE Trans. Med. Imag., vol. 42, no. 3, pp. 215-229, Jun. 2024.
W. Liu et al., "Adaptive Feature Extraction Strategies in Cross-Mobility Medical Imaging," J. Biomed. Inform., vol. 97, pp. 103-118, Mar. 2024.
A.E. Kavur, M. A. Selver, O. Dicle, M. Barış, N.S. Gezer. CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data (Version v1.03) [Data set]. Apr. 2019. Zenodo. http://doi.org/10.5281/zenodo.3362844.
R. Patel and S. Kumar, "Advanced Computational Techniques in Multi-Modal Image Alignment," Med. Image Anal., vol. 38, no. 2, pp. 87-104, Apr. 2024.