Changed Detection Based on Patch Robust Principal Component Analysis
Abstract
:1. Introduction
2. Preprocessing
2.1. Intensity Correction
2.2. Linear Interpolation
3. Methodology
3.1. Low-Rank Decomposition Modeling
3.2. Patch Low-Rank Decomposition Modeling
3.3. Algorithm and Flow Chart
Algorithm 1: Algorithm procedure of P-RPCA |
Input: A registered pair of fundus image and ; Output: Change detection of according to
|
4. Experiments and Discussion
4.1. Data
4.2. Validation Measurement
4.3. Results with P-RPCA Method
4.4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Acharya, U.R.; Lim, C.M.; Ng, E.Y.K. Computer-based detection of diabetes retinopathy stages using digital fundus images. Proc. Inst. Mech. Eng. 2009, 223, 545–553. [Google Scholar] [CrossRef] [PubMed]
- Mookiah, M.R.K.; Acharya, U.R.; Chua, C.K.; Lim, C.M.; Ng, E.Y.K.; Laude, A. Computer-aided diagnosis of diabetic retinopathy: A review. Comput. Biol. Med. 2013, 43, 2136–2155. [Google Scholar] [CrossRef] [PubMed]
- Faust, O.; Rajendra, A.U.; Ng, E.Y.K.; Ng, K.H.; Suri, J.S. Algorithms for the Automated Detection of Diabetic Retinopathy Using Digital Fundus Images: A Review. J. Med. Syst. 2012, 36, 145–157. [Google Scholar] [CrossRef] [PubMed]
- Winder, R.J.; Morrow, P.J.; McRitchie, I.N.; Bailie, J.R.; Hart, P.M. Algorithms for digital image processing in diabetic retinopathy. Comput. Med. Imaging Graph. 2009, 33, 608–622. [Google Scholar] [CrossRef]
- Radke, R.J.; Andra, S.; Al-Kofahi, O.; Roysam, B. Image change detection algorithms: A systematic survey. IEEE Trans. Image Process. 2005, 14, 294–307. [Google Scholar] [CrossRef]
- Goyette, N.; Jodoin, P.M.; Porikli, F.; Konrad, J.; Ishwar, P. A novel video dataset for change detection benchmarking. IEEE Trans. Image Process. 2014, 23, 4663–4679. [Google Scholar] [CrossRef] [PubMed]
- Tian, F.P.; Feng, W.; Zhang, Q.; Wang, X.; Sun, J.Z.; Loia, V.; Liu, Z.Q. Active Camera Relocalization from a Single Reference Image without Hand-Eye Calibration. IEEE Trans. Pattern Anal. Mach. Intell. 2018, 41, 2791–2806. [Google Scholar] [CrossRef]
- Abràmoff, M.D.; Garvin, M.K.; Sonka, M. Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 2010, 3, 169–208. [Google Scholar] [CrossRef] [Green Version]
- Patton, N.; Aslam, T.M.; MacGillivray, T.; Deary, I.J.; Dhillon, B.; Eikelboom, R.H.; Yogesan, K.; Constable, I.J. Retinal image analysis: Concepts, applications and potential. Prog. Retin. Eye Res. 2006, 1, 99–127. [Google Scholar] [CrossRef] [PubMed]
- Fu, Y.; Wang, C.; Wang, Y.; Chen, B.; Peng, Q.; Wang, L. Automatic Detection of Longitudinal Changes for Retinal Fundus Images Based on Low-Rank Decomposition. J. Med. Imaging Health Inform. 2018, 8, 284–294. [Google Scholar] [CrossRef]
- Narasimha-Iyer, H.; Can, A.; Roysam, B.; Stewart, V.; Tanenbaum, H.L.; Majerovics, A.; Singh, H. Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy. IEEE Trans. Biomed. Eng. 2006, 53, 1084–1098. [Google Scholar] [CrossRef] [PubMed]
- Gong, M.; Zhao, J.; Liu, J.; Miao, Q.; Jiao, L. Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks. IEEE Trans. Neural Netw. Learn. Syst. 2016, 27, 125–138. [Google Scholar] [CrossRef] [PubMed]
- Chen, H.; Qi, Z.; Shi, Z. Remote Sensing Image Change Detection With Transformers. IEEE Trans. Geosci. Remote Sens. 2022, 60, 1–14. [Google Scholar] [CrossRef]
- Zhang, C.; Wang, L.; Cheng, S.; Li, Y. SwinSUNet: Pure Transformer Network for Remote Sensing Image Change Detection. IEEE Trans. Geosci. Remote Sens. 2022, 60, 1–13. [Google Scholar] [CrossRef]
- Gong, M.; Li, Y.; Jiao, L.; Jia, M.; Su, L. SAR change detection based on intensity and texture changes. ISPRS J. Photogramm. Remote Sens. 2014, 93, 123–135. [Google Scholar] [CrossRef]
- Gong, M.; Zhou, Z.; Ma, J. Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans. Image Process. 2012, 21, 2141–2151. [Google Scholar] [CrossRef]
- Soomro, T.A.; Gao, J.; Khan, T.; Hani, A.F.M.; Khan, M.A.; Paul, M. Computerised approaches for the detection of diabetic retinopathy using retinal fundus images: A survey. Pattern Anal. Appl. 2017, 20, 927–961. [Google Scholar] [CrossRef]
- Fu, Y.; Wang, Y.; Zhong, Y.; Fu, D.; Peng, Q. Change detection based on tensor RPCA for longitudinal retinal fundus images. Neurocomputing 2020, 387, 1–12. [Google Scholar] [CrossRef]
- Guyon, C.; Bouwmans, T.; Zahzah, E.H. Robust Principal Component Analysis for Background Subtraction: Systematic Evaluation and Comparative Analysis. Princ. Compon. Anal. 2012, 10, 223–238. [Google Scholar]
- Cao, W.; Wang, Y.; Sun, J.; Meng, D.; Yang, C.; Cichocki, A.; Xu, Z. Total Variation Regularized Tensor RPCA for Background Subtraction From Compressive Measurements. IEEE Trans. Image Process. 2016, 25, 4075–4090. [Google Scholar] [CrossRef]
- Sopharak, A.; Nwe, K.T.; Moe, Y.A.; Dailey, M.N.; Uyyanonvara, B.; Automatic Exudate Detection with a Naive Bayes Classifier. Imaging in the Eye, IV; 2008. Available online: https://www.semanticscholar.org/paper/Automatic-Exudate-Detection-with-a-Naive-Bayes-Sopharak-Nwe/ac76ccce144112e819dd5f9a6601a25888bfd871 (accessed on 15 June 2022).
- Aquino, A.; Gegundez-Arias, M.E.; Marin, D. Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques. IEEE Trans. Med. Imaging 2010, 29, 1860–1869. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Usher, D.; Dumskyj, M.; Himaga, M.; Williamson, T.H.; Nussey, S.; Boyce, J. Automated detection of diabetic retinopathy in digital retinal images: A tool for diabetic retinopathy screening. Diabet. Med. 2004, 21, 84–90. [Google Scholar] [CrossRef] [PubMed]
- Mendonça, A.M.; Campilho, A. Segmentation of Retinal Blood Vessels by Combining the Detection of Centerlines and Morphological Reconstruction. IEEE Trans. Med. Imaging 2006, 25, 1200–1213. [Google Scholar] [CrossRef] [PubMed]
- Staal, J.; Abràmoff, M.D.; Niemeijer, M.; Viergever, M.A.; Van Ginneken, B. Ridge-based vessel segmentation in color images of the retina Centerlines and Morphological Reconstruction. IEEE Trans. Med. Imaging 2004, 23, 501–509. [Google Scholar] [CrossRef] [PubMed]
- Candès, E.J.; Wakin, M.B. An introduction to compressive sampling. IEEE Signal Process. Mag. 2008, 25, 21–30. [Google Scholar] [CrossRef]
- Fornasier, M.; Rauhut, H. Compressive Sensing. Handb. Math. Methods Imaging 2015, 1, 187–229. [Google Scholar]
- Candès, E.J.; Li, X.; Ma, Y.; Wright, J.A. Robust principal component analysis? J. ACM (JACM) 2011, 58, 1–37. [Google Scholar] [CrossRef]
- Ding, X.; He, L.; Carin, L. Bayesian Robust Principal Component Analysis. IEEE Trans. Image Process. 2011, 20, 3419–3430. [Google Scholar] [CrossRef] [Green Version]
- Tan, W.T.; Cheung, G.; Ma, Y. Face recovery in conference video streaming using robust principal component analysis. In Proceedings of the 2011 18th IEEE International Conference on Image Processing, Brussels, Belgium, 11–14 September 2011. [Google Scholar]
- Lin, Z.; Chen, M.; Ma, Y. The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices. arXiv 2010, arXiv:1009.5055. [Google Scholar]
- Chen, J.; Tian, J.; Lee, N.; Zheng, J.; Smith, R.T.; Laine, A.F. A partial intensity invariant feature descriptor for multimodal retinal image registration. IEEE Trans. Biomed. Eng. 2010, 57, 1707–1718. [Google Scholar] [CrossRef] [Green Version]
- Sun, Y.; Lei, L.; Guan, D.; Kuang, G. Iterative Robust Graph for Unsupervised Change Detection of Heterogeneous Remote Sensing Images. IEEE Trans. Image Process. 2021, 30, 6277–6291. [Google Scholar] [CrossRef] [PubMed]
- Sun, Y.; Lei, L.; Li, X.; Sun, H.; Kuang, G. Nonlocal patch similarity based heterogeneous remote sensing change detection. Pattern Recognit. 2021, 109, 107598. [Google Scholar] [CrossRef]
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Zhu, W.; Zhang, Z.; Zhao, X.; Fu, Y. Changed Detection Based on Patch Robust Principal Component Analysis. Appl. Sci. 2022, 12, 7713. https://doi.org/10.3390/app12157713
Zhu W, Zhang Z, Zhao X, Fu Y. Changed Detection Based on Patch Robust Principal Component Analysis. Applied Sciences. 2022; 12(15):7713. https://doi.org/10.3390/app12157713
Chicago/Turabian StyleZhu, Wenqi, Zili Zhang, Xing Zhao, and Yinghua Fu. 2022. "Changed Detection Based on Patch Robust Principal Component Analysis" Applied Sciences 12, no. 15: 7713. https://doi.org/10.3390/app12157713