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Cerebrovascular Segmentation Based on Edge Preserving Filters Technique in Magnetic Resonance Angiography Images: A Systematic Review
Nur Intan Raihana Ruhaiyem, Nur Atifah Hammade
Pages - 48 - 67     |    Revised - 30-09-2021     |    Published - 31-10-2021
Volume - 15   Issue - 4    |    Publication Date - October 2021  Table of Contents
Cerebrovascular Segmentation, Time-of-flight Magnetic Resonance Angiography, Signal-to-noise Ratio, Vessel Enhancement, Cerebral Small Vessel Disease.
Magnetic resonance angiography (MRA) is an emerging magnetic resonance imaging method for the detection and diagnosis of cerebrovascular diseases including cerebral small vessel disease (CSVD). However, the challenges to extract cerebrovascular structures are recognised, especially from the time-of flight MRA (TOF-MRA) images due to the intricate vascular structures and inherent noise. This paper presents a comprehensive review on image processing pipeline which have been successfully applied on CSVD images such as Computed Tomography (CT) scan, Computed Tomography Angiography (CTA), Digital Subtraction Angiography (DSA), Magnetic Resonance Angiography (MRA), and Magnetic Resonance Imaging (MRI), review on various denoising filters in CSVD images such as Nonlocal Mean (NLM) filter, Multiscale filter, Anisotropic Diffusion filter (ADF), Bilateral filter (BF), Smoothing filter, 3D Steerable filter, Moving Average filter, Trilateral filter, Wiener filter, Blockmatching and 3D filtering (BM3D), Non-linear quasi-Newton method (L-BFGS), and Histogram Equalization (HE). This review also features edge preserving filter (EPF) techniques to reduce noises while preserving the edges from TOF-MRA images including ADF, BF, NMF, Mean Shift filter (MSF), and Sigma filter (SF).
1 refSeek 
2 BibSonomy 
3 Doc Player 
4 Scribd 
5 SlideShare 
Abdallah, M. B., Azar, A. T., Guedri, H., Malek, J., & Belmabrouk, H. (2018). NoiseEstimation-Based Anisotropic Diffusion Approach for Retinal Blood Vessel Segmentation. Neural Computing and Applications, 29(8), 159-180.
Abdallah, M. B., Malek, J., Azar, A. T., Belmabrouk, H., Monreal, J. E., & Krissian, K. (2016). Adaptive Noise-Reducing Anisotropic Diffusion Filter. Neural Computing and Applications, 27(5), 1273-1300.
Abdallah, M. B., Malek, J., Azar, A. T., Montesinos, P., Belmabrouk, H., Esclarín Monreal, J., & Krissian, K. (2015). Automatic Extraction of Blood Vessels in the Retinal Vascular Tree Using Multiscale Medialness. International Journal of Biomedical Imaging, 519024. https://doi.org/10.1155/2015/519024
Ajam, A., Aziz, A. A., Asirvadam, V. S., Izhar, L. I., & Muda, S. (2016). Cerebral Vessel Enhancement Using Bilateral and Hessian-Based Filter. In 2016 6th International Conference on Intelligent and Advanced Systems (ICIAS), 1-6.
Ajam, A., Aziz, A. A., Asirvadam, V. S., Muda, A. S., Faye, I., & Gardezi, S. J. S. (2017). A Review on Segmentation and Modeling of Cerebral Vasculature for Surgical Planning. IEEE Access, 5, 15222-15240.
Aldroubi, A. (2017). Wavelets in Medicine and Biology. Routledge.
Anantrasirichai, N., Nicholson, L., Morgan, J. E., Erchova, I., Mortlock, K., North, R. V., & Achim, A. (2014). Adaptive-Weighted Bilateral Filtering and Other Pre-Processing Techniques for Optical Coherence Tomography. Computerized Medical Imaging and Graphics, 38(6), 526-539.
Badgujar, R. D., & Deore, P. J. (2018). Region Growing Based Segmentation Using Forstner Corner Detection Theory for Accurate Microaneurysms Detection in Retinal Fundus Images. In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 1-5.
Borrelli, P., Palma, G., Comerci, M., & Alfano, B. (2014a). Unbiased Noise Estimation and Denoising in Parallel Magnetic Resonance Imaging. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1230-1234.
Borrelli, P., Tedeschi, E., Cocozza, S., Russo, C., Salvatore, M., Palma, G., & Haacke, E. M. (2014b). Improving SNR in Susceptibility Weighted Imaging by a NLM-Based Denoising Scheme. In 2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings, 346-350.
Buades, A., Coll, B., & Morel, J. M. (2005). A Review of Image Denoising Algorithms, with a New One. Multiscale Modeling and Simulation, 4(2), 490-530.
Bullitt E., Gerig, G., Pizer, S. M., Lin, W., & Aylward, S. R. (2003). Measuring Tortuosity of the Intracerebral Vasculature from MRA Images. IEEE Transactions on Medical Imaging, 22(9), 1163-1171.
Carr, J. C., & Carroll, T. J. (Eds.). (2011). Magnetic Resonance Angiography: Principles and Applications. Springer Science & Business Media.
Chang, H. H., Hsieh, T. J., Ting, Y. N., & Chu, W. C. (2011). Rician Noise Removal in MR Images Using an Adaptive Trilateral Filter. In 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI), 1, 467-471.
Che Mohd Nasir, C. M. N., Damodaran, T.,Yusof, S. R., Norazit, A., Chilla, G., Huen, I., K. N. B. P., Mohamed Ibrahim, N., Mustapha, M. (2021). Aberrant Neurogliovascular Unit Dynamics in Cerebral Small Vessel Disease: A Rheological Clue to Vascular Parkinsonism. Pharmaceutics, 13(8):1207, 1999-4923.
Chen, G., Zhang, P., Wu, Y., Shen, D., & Yap, P. T. (2015a). Collaborative Non-Local Means Denoising of Magnetic Resonance Images. In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 564-567.
Chen, H., & Hale, J. (1995). An Algorithm for MR Angiography Image Enhancement. Magnetic Resonance in Medicine, 33(4), 534-540.
Chen, K., Yin, Q., Jia, X., & Lu, M. (2015b). Vessel Enhancement Based an Improved Bilateral Filter for Coronary Angiography. In 2015 8th International Congress on Image and Signal Processing (CISP), 308-312.
China, D., Mitra, P., Chakraborty, C., & Mandana, K. M. (2015). Wavelet Based Non-Local Means Filter for Despeckling of Intravascular Ultrasound Image. In 2015 International Conference on Advances in Computing, Communications, and Informatics (ICACCI), 1361-1365.
Chung A. C., Noble, J. A., & Summers, P. (2004). Vascular Segmentation of Phase Contrast Magnetic Resonance Angiograms Based on Statistical Mixture Modeling and Local Phase Coherence. IEEE Transactions on Medical Imaging, 23(12), 1490-1507.
Coupé, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., & Barillot, C. (2008). An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images. IEEE Transactions on Medical Imaging, 27(4), 425-441.
Dash, J., & Bhoi, N. (2017). A Thresholding Based Technique to Extract Retinal Blood Vessels from Fundus Images. Future Computing and Informatics Journal, 2(2), 103-109.
El-Baz, A., Elnakib, A., Khalifa, F., El-Ghar, M. A., Mcclure, P., Soliman, A., & Gimelrfarb, G. (2012). Precise Segmentation of 3-D Magnetic Resonance Angiography. IEEE Transactions on Biomedical Engineering, 59(7), 2019-2029.
El-Baz, A., Gimel'farb, G., Kumar, V., Falk, R., & El-Ghar, M. A. (2009). 3D Joint MarkovGibbs Model for Segmenting the Blood Vessels from MRA. In 2009 IEEE International Symposium on Biomedical Imaging: from Nano to Macro, 1366-1369.
Elahi, P., Beheshti, S., & Hashemi, M. (2014). BM3D MRI denoising Equipped with Noise Invalidation Technique. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 6612-6616.
Fu, B., Zhao, X. Y., Ren, Y. G., Li, X. M., & Wang, X. H. (2018). A Salt and Pepper Noise Image Denoising Method Based on the Generative Classification. arXiv preprint arXiv:1807.05478.
Goyal, B., Agrawal, S., & Sohi, B. S. (2018). Noise Issues Prevailing in Various Types of Medical Images. Biomedical & Pharmacology Journal, 11(3), 1227.
Gupta, T. K. (2013). Radiation, Ionization, and Detection in Nuclear Medicine. Heidelberg: Springer.
Han, H., & Sohn, K. (2009). Automatic Illumination and Color Compensation Using Mean Shift and Sigma Filter. IEEE Transactions on Consumer Electronics, 55(3), 978-986.
Hao, J., Shen, Y., & Wang, Q. (2007). Segmentation for MRA Image: An Improved Level-Set Approach. IEEE Transactions on Instrumentation and Measurement, 56(4), 1316-1321.
Hartmann, U. (Ed.). (2005). Aktuelle Methoden Der Laser-Und Medizinphysik: Tagungsband Der 2.
Hartung, M. P., Grist, T. M., & François, C. J. (2011). Magnetic Resonance Angiography: Current Status and Future Directions. Journal of Cardiovascular Magnetic Resonance, 13(1), 19.
Hassani, A. E., & Majda, A. (2016). Efficient Image Denoising Method Based on Mathematical Morphology Reconstruction and the Non-Local Means Filter for the MRI of the Head. In 2016 4th IEEE International Colloquium on Information Science and Technology (CIST), 422-427.
He, Y., Zheng, Y., Zhao, Y., Ren, Y., Lian, J., & Gee, J. (2017). Retinal Image Denoising via Bilateral Filter with a Spatial Kernel of Optimally Oriented Line Spread Function. Computational and Mathematical Methods in Medicine.
Hibet-Allah, O., Hajer, J., & Kamel, H. (2016). Vascular Tree Segmentation in MRA Images Using Hessian-Based Multiscale Filtering and Local Entropy Thresholding. In 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 325-329.
Hibet-Allah, O., Hejer, J., & Kamel, H. (2017). 3D MRA Segmentation Using the Vesselness Filter. In 2017 40th International Conference on Telecommunications and Signal Processing (TSP), 615-620.
Hu, J., Pu, Y., Wu, X., Zhang, Y., & Zhou, J. (2012). Improved DCT-Based Nonlocal Means Filter for MR Images Denoising. Computational and Mathematical Methods in Medicine.
Jayabal, P., & Damodaran, N. (2015). Rician Noise Removal and Straightening of Blood Vessel in MR Images. Journal of Biomedical Engineering and Medical Imaging, 2(6), 28-28.
Jaybhay, J., & Shastri, R. (2015). A Study of Speckle Noise Reduction Filters. Signal & Image Processing: An International Journal (SIPIJ), 6(3), 71-80.
Kandil, H., Soliman, A., Fraiwan, L., Shalaby, A., Mahmoud, A., Eltanboly, A., & El-Baz, A. (2018). A Novel MRA Framework Based on Integrated Global and Local Analysis for Accurate Segmentation of the Cerebral Vascular System. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 1365-1368.
Kaur, K., Singh, B., & Kaur, M. (2012). Speckle Noise Reduction Using 2-D FFT in Ultrasound Images. International Journal of Advances in Engineering & Technology, 4(2), 79.
Krissian, K. (2002). Flux-Based Anisotropic Diffusion Applied to Enhancement of 3-D Angiogram. IEEE Transactions on Medical Imaging, 21(11), 1440-1442.
Lee, J. S. (1983). Digital Image Smoothing and the Sigma Filter. Computer Vision, Graphics, and Image Processing, 24(2), 255-269.
Lee, J. S., Wen, J. H., Ainsworth, T. L., Chen, K. S., & Chen, A. J. (2008). Improved Sigma Filter for Speckle Filtering of SAR Imagery. IEEE Transactions on Geoscience and Remote Sensing, 47(1), 202-213.
Liang, H.; Li, N.; Zhao, S. (2021) Salt and Pepper Noise Removal Method Based on a Detail-Aware Filter. Symmetry, 13(3):515, 2073-8994.
Liao, W., Rohr, K., Kang, C. K., Cho, Z. H., & Wörz, S. (2011). A Generative MRF Approach for Automatic 3D Segmentation of Cerebral Vasculature from 7 Tesla MRA Images. In 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2041-2044.
Lu, P., Xia, J., Li, Z., Xiong, J., Yang, J., Zhou, S., & Wang, C. (2016). A Vessel Segmentation Method for Multi-Modality Angiographic Images Based on Multi-Scale Filtering and Statistical Models. Biomedical Engineering Online, 15(1), 120.
Lukin, V. V., Ponomarenko, N. N., Kuosmanen, P. S., & Astola, J. T. (1996). Modified Sigma Filter for Processing Images Corrupted by Multiplicative and Impulsive Noise. In 1996 8th European Signal Processing Conference (EUSIPCO 1996), 1-4.
Macdonald, L. (2006). Digital Heritage. Routledge.
Manniesing, R., Viergever, M. A., & Niessen, W. J. (2006). Vessel Enhancing Diffusion: A Scale Space Representation of Vessel Structures. Medical Image Analysis, 10(6), 815-825.
Moccia, S., De Momi, E., El Hadji, S., & Mattos, L. S. (2018). Blood Vessel Segmentation Algorithms—Review of Methods, Datasets and Evaluation Metrics. Computer Methods and Programs in Biomedicine, 158, 71-91.
Mohan, J., Guo, Y., Krishnaveni, V., & Jeganathan, K. (2012). MRI Denoising Based on Neutrosophic Wiener Filtering. In 2012 IEEE International Conference on Imaging Systems and Techniques Proceedings, 327-331.
Morales, R. R., Domínguez, D., Torres, E., & Sossa, J. H. (2012). Image Segmentation through an Iterative Algorithm of the Mean Shift. In Advances in Image Segmentation. Intechopen.
Mou, X., Wang, X., Wu, Z., Wang, X., & Zhou, M. (2015). An Automatic Ehealth Platform for Cardiovascular and Cerebrovascular Disease Detection. In 2015 International Symposium on Bioelectronics and Bioinformatics (ISBB), 63-66.
Nowak, R. D. (1999). Wavelet-Based Rician Noise Removal for Magnetic Resonance Imaging. IEEE Transactions on Image Processing, 8(10), 1408-1419.
Pantoni, L. (2010). Cerebral Small Vessel Disease: From Pathogenesis and Clinical Characteristics to Therapeutic Challenges. The Lancet Neurology, 9(7), 689-701.
Pantoni, L., & Gorelick, P. B. (Eds.). (2014). Cerebral Small Vessel Disease. Cambridge University Press.
Phellan, R., Lindner, T., Helle, M., Falcão, A. X., & Forkert, N. D. (2017). Automatic Temporal Segmentation of Vessels of the Brain Using 4D ASL MRA Images. IEEE Transactions on Biomedical Engineering, 65(7), 1486-1494.
Prima, S., & Commowick, O. (2013). Using Bilateral Symmetry to Improve Non-Local Means Denoising of MR Brain Images. In 2013 IEEE 10th International Symposium on Biomedical Imaging, 1231-1234.
Rajab, M. I. (2016). Performance Evaluation of Image Edge Detection Techniques. International Journal of Computer Science and Security, 10(5), 170-185.
Rani, V. (2013). A Brief Study of Various Noise Model and Filtering Techniques. Journal of Global Research in Computer Science, 4(4), 166-171.
Ren, R., Guo, Z., Jia, Z., Yang, J., Kasabov, N. K., Li, C. (2019). Speckle Noise Removal in Image-based Detection of Refractive Index Changes in Porous Silicon Microarrays. Scientific Reports, 9, 15001.
Rodríguez, R. (2008). Binarization of Medical Images Based on the Recursive Application of Mean Shift Filtering: Another Algorithm. Advances and Applications in Bioinformatics and Chemistry (AABC), 1, 1–12.
Romdhane, F., Benzarti, F., & Amiri, H. (2014). 3D Medical Images Denoising. In International Image Processing, Applications and Systems Conference, 1-5.
Rosamond, W., Flegal, K., Friday, G., Furie, K., Go, A., & Greenlund, K. (2007). Heart Disease and Stroke Statistics--2007 Update: A Report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation, 115(5), E69–E171.
Russo, F. (2018). On the Accuracy of Denoising Algorithms in Medical Imaging: A Case Study. In 2018 IEEE International Symposium on Medical Measurements and Applications (Memea), 1-6.
Sanya, W., Bajpai, G., Amour, M., Alawi, M., & Adnan, A. (2021). A Novel Advanced Approach Using Morphological Image Processing Technique for Early Detection of Diabetes Retinopathy. International Journal of Image Processing, 15(3), 22-36.
Selçuk, T., Tuncer, S. A., Tekinalp, M., & Alkan, A. (2017). Non-Local Means Based Image Enhancement on Coronary Angiography Images. In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), 1-4.
Selvathi, D., Selvi, S. T., & Malar, C. L. S. (2012). The SURE-LET Approach for MR Brain Image Denoising Using Different Shrinkage Rules. In Advancing Technologies and Intelligence in Healthcare and Clinical Environments Breakthroughs, 227-235.
Shi, F., & Yang, J. (2009). Multiscale Vesselness Based Bilateral Filter for Blood Vessel Enhancement. Electronics Letters, 45(23), 1152-1154.
Shi, Y., & Wardlaw, J. M. (2016). Update on Cerebral Small Vessel Disease: A Dynamic WholeBrain Disease. Stroke and Vascular Neurology, 1(3), 83-92.
Shruthi, B., Renukalatha, S., & Siddappa, D. M. (2015). Speckle Noise Reduction in Ultrasound Images-A Review. International Journal of Engineering Research and Technology, 4(02).
Siva Sundhara Raja, D., & Vasuki, S. (2015). Automatic Detection of Blood Vessels in Retinal Images for Diabetic Retinopathy Diagnosis. Computational and Mathematical Methods in Medicine.
Solomon, J., Crane, K., Butscher, A., & Wojtan, C. (2014). A General Framework for Bilateral and Mean Shift Filtering, 1(2), 3.
Soomro, T. A., Paul, M., Gao, J., & Zheng, L. (2017). Retinal Blood Vessel Extraction Method Based on Basic Filtering Schemes. In 2017 IEEE International Conference on Image Processing (ICIP), 4422-4426.
Story, C. M. (2016). Magnetic Resonance Angiography (MRA). Healthline. Retrieved from https://www.healthline.com/health/magnetic-resonance-angiography.
Sun, X., Chai, Z., Miao, C., Jiang, Y., Duan, Z. Y., Wang, L. G., & Chang, S. H. (2010). Retinal Vessel Tracking Using Bilateral Filter Based on Canny Method. In 2010 International Conference on Audio, Language, and Image Processing, 1678-1682.
Sun, Y., & Parker, D. L. (1999). Performance Analysis of Maximum Intensity Projection Algorithm for Display of MRA Images. IEEE Transactions on Medical Imaging, 18(12), 1154-1169.
Tan, J. (Ed.). (2012). Advancing Technologies and Intelligence in Healthcare and Clinical Environments Breakthroughs. IGI Global.
Tek, H., Comaniciu, D., & Williams, J. P. (2001). Vessel Detection by Mean Shift Based Ray Propagation. In Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001), 228-235.
Tsai, D. Y., Lee, Y., & Takahashi, N. (2006). An Adaptive Enhancement Algorithm for CT Brain Images. In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 3398-3401.
Tsou, C. H., Lu, Y. C., Yuan, A., Chang, Y. C., & Chen, C. M. (2015). A Heuristic Framework for Image Filtering and Segmentation: Application to Blood Vessel Immunohistochemistry. Analytical Cellular Pathology. doi: 10.1155/2015/589158.
Tulin, I. J., Imam, M. Z., & Das, P. P. (2017). Computer-Aided Kidney Segmentation on Abdominal CT Images Using Fuzzy Based Denoising for Gaussian Noise. International Journal of Neurosciece and Behavioral Science, 5(4): 59-65.
Uemori, N., Sugitani, Y., Tamada, H., Ohi, Y., & Ishikawa, C. (2014). Incidence and Clinical Predictors of Stent Restenosis and Early Stent Occlusion in Patients with Acute Myocardial Infarction Treated by Bare Metal Stents: Importance of Infarct Location and Serum Creatinine Level. Angiol, 2(136), 2.
Volkau, I., Ng, T. T., Marchenko, Y., & Nowinski, W. L. (2008). On Geometric Modeling of the Human Intracranial Venous System. IEEE Transactions on Medical Imaging, 27(6), 745751.
Vyas, A., & Paik, J. (2018). Applications of Multiscale Transforms to Image Denoising: Survey. In 2018 International Conference on Electronics, Information, and Communication (ICEIC), 1-3.
Weiping, Z., & Huazhong, S. (2006). Detection of Cerebral Vessels in MRA Using 3D Steerable Filters. In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 3249-3252.
Xu, Z., & Shi, Q. (2016). Denoising Model for Parallel Magnetic Resonance Imaging Images Using Higher-Order Markov Random Fields. IET Image Processing, 10(12), 962-970.
Zhang, B., Liu, S., & Zhou, S. (2020). Cerebrovascular Segmentation from TOF-MRA using Model- and Data-driven Method via Sparse Labels. Neurocomputing, 380, 162-179.
Zhang, M., & Gunturk, B. K. (2008). Multiresolution Bilateral Filtering for Image Denoising. IEEE Transactions on Image Processing, 17(12), 2324-2333.
Zhang, Y., Jiang, H., & Ma, L. (2018). Blood Vessel Segmentation Based on Digital Subtraction Angiography Sequence. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2049-2054.
Zhao, S., Wu, Z., & Zhou, M. (2010). A Multi-Scale Method for Extraction of Cerebral Blood Vessels. In 2010 IEEE International Conference on Progress in Informatics and Computing, 2, 1280-1283.
Zubair, A. R., & Busari, H. O. (2018). Robustness of Median Filter for Suppression of Salt and Pepper Noise (SPN) and Random Valued Impulse Noise (RVIN). International Journal of Image Processing, 12(1), 12-27.
Dr. Nur Intan Raihana Ruhaiyem
School of Computer Sciences, Universiti Sains Malaysia, USM, 11800, Penang - Malaysia
Miss Nur Atifah Hammade
School of Computer Sciences, Universiti Sains Malaysia, USM, 11800, Penang - Malaysia