eISSN: 3023-3267 editor@aimjournals.com
All Journals
Submit your article

Critique Open Research & Review

Open Access Peer Review International
Open Access

Fusion of Frequency-Domain Network Analysis and Deep Visual Descriptors for Recognition of Structural Configurations in the Cerebral Vascular Loop

DOI
PDF
Michael J. Carter 1 ,
4 Faculty of Electrical and Information Engineering Northern Coast University Sydney, Australia

Abstract

Accurate recognition of structural configurations in the cerebral vascular loop, commonly known as the Circle of Willis, is essential for understanding cerebrovascular health, detecting anatomical variations, and predicting neurological risks. Variations in this arterial structure are strongly associated with ischemic stroke, aneurysm formation, and other vascular disorders, making automated and reliable classification methods highly valuable in modern neuroimaging analysis. Traditional approaches rely on manual inspection or purely image-based deep learning techniques, which may fail to capture the complex topological relationships between vessels. To address these limitations, this research proposes a hybrid framework that combines frequency-domain network analysis based on spectral graph theory with deep visual descriptors extracted from convolutional neural networks.

The proposed method models the vascular loop as a graph structure, where arteries are treated as nodes and their connections as edges, enabling the use of spectral analysis to capture global topological properties. Frequency-domain representations derived from graph Laplacian eigenvalues provide structural information that cannot be obtained through spatial image features alone. In parallel, convolutional neural networks are used to extract high-level visual descriptors from magnetic resonance angiography images, capturing local anatomical details. A fusion strategy integrates spectral graph features with deep visual representations to create a unified descriptor capable of identifying anatomical variants with high reliability.

The framework is evaluated using publicly available vascular imaging datasets and challenge benchmarks related to Circle of Willis classification. Experimental results show that combining spectral graph features with deep visual descriptors improves classification accuracy, robustness to noise, and generalization across different imaging conditions compared to single-modality methods. The approach also provides better interpretability because graph-based representations preserve anatomical relationships between vessels.

This study demonstrates that integrating frequency-domain network analysis with deep learning features offers an effective solution for automated recognition of cerebral vascular loop configurations. The proposed method has potential applications in clinical decision support, large-scale screening, and research on cerebrovascular diseases, where accurate structural identification is critical for diagnosis and risk assessment.

Keywords

Circle of Willis classification, spectral graph theory, convolutional neural networks, vascular topology analysis

References

📄 F. Abdullah, A. Jamil, E. M. Alazzawi, and A. A. Hameed, “Exploring deep learning-based approaches for brain tumor diagnosis from MRI images,” in Proc. IEEE 3rd Int. Conf. Comput. Mach. Intell., 2024, pp. 1–11, doi: 10.1109/ICMI60790.2024.10585851.
📄 A. Abrol, H. Rokham, and V. D. Calhoun, “Diagnostic and prognostic classification of brain disorders using residual learning on structural MRI data,” in Proc. 41st Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., Berlin, Germany, 2019, pp. 4084–4088, doi: 10.1109/EMBC.2019.8857902.
📄 A. Bessadok, M. A. Mahjoub, and I. Rekik, “Graph neural networks in network neuroscience,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 5, pp. 5833–5848, May 2023, doi: 10.1109/TPAMI.2022.3209686.
📄 Buslaev et al., “Albumentations: Fast and flexible image augmentations,” Information, vol. 11, no. 2, 2020, Art. no. 125, doi: 10.3390/info11020125.
📄 F. R. K. Chung, Spectral Graph Theory. Providence, RI, USA : Amer. Math. Soc., 1997.
📄 CROWN Challenge, “Circle of Willis intracranial artery classification and quantification (CROWN) challenge,” 2023. Accessed: May 20, 2025. [Online]. Available: https://crown.isi.uu.nl
📄 L. Feng, H. J. Mao, D. D. Zhang, Y. C. Zhu, and F. Han, “Anatomical variations in the circle of willis and the formation and rupture of intracranial aneurysms: A systematic review and meta-analysis,” Front. Neurol., vol. 13, 2023, Art. no. 1098950, doi: 10.3389/fneur.2022.1098950.
📄 L. Feng et al., “Association between anatomical variations of the circle of willis and covert vascular brain injury in the general population,” Cerebrovascular Dis., vol. 52, no. 4, pp. 480–486, 2023, doi: 10.1159/000527432.
📄 B. A. Fabian and C.-C. Vancea, “Efficient methods for improving brain tumors diagnosis in MRI scans using deep learning,” in Proc. IEEE 20th Int. Conf. Intell. Comput. Commun. Process., Cluj-Napoca, Romania, 2024, pp. 1–8, doi: 10.1109/ICCP63557.2024.10793036.
📄 D. K. Hammond, P. Vandergheynst, and R. Gribonval, “Wavelets on graphs via spectral graph theory,” Appl. Comput. Harmon. Anal., vol. 30, no. 2, pp. 129–150, 2011, doi: 10.1016/j.acha.2010.04.005.
📄 K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2016, pp. 770–778, doi: 10.1109/CVPR.2016.90.
📄 S. Iqbal, “A comprehensive study of the anatomical variations of the circle of willis in adult human brains,” J. Clin. Diagn. Res., vol. 7, no. 11, pp. 2423–2427, 2013, doi: 10.7860/JCDR/2013/6580.3563.
📄 K. N. Kayembe et al., “Cerebral aneurysms and variations in the circle of willis,” Stroke, vol. 15, no. 5, pp. 846–850, 1984, doi: 10.1161/01.str.15.5.846.
📄 D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proc. 3rd Int. Conf. Learn. Representations, 2015.
📄 T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in Proc. Int. Conf. Learn. Representations, 2017.
📄 H. Lippert and R. Pabst., “Cerebral arterial circle (circle of Willis),” in Arterial Variations in Manjf, 1st ed. Munchen, Germany : Bergmann-Verlag, 1985, pp. 92–93.
📄 V. Luxburg and Ulrike, “A tutorial on spectral clustering,” Statist. Comput., vol. 17, no. 4, pp. 395–416, 2007.
📄 R. Nader, R. Bourcier, and F. Autrusseau, “Using deep learning for an automatic detection and classification of the vascular bifurcations along the circle of Willis,” Med. Image Anal., vol. 89, 2023, Art. no. 102919, doi: 10.1016/j.media.2023.102919.
📄 M. Oumer, M. Alemayehu, and A. Muche, “Association between circle of Willis and Ischemic Stroke: A systematic review and meta-analysis,” BMC Neurosci., vol. 22, no. 1, 2021, Art. no. 3, doi: 10.1186/s12868-021-00609-4.
📄 S. Parisot et al., “Disease prediction using graph convolutional networks: Application to autism spectrum disorder and alzheimer’s disease,” Med. Image Anal., vol. 48, pp. 117–130, 2018, doi: 10.1016/j.media.2018.06.001.
📄 A. Shah and S. Aran, “A review of magnetic resonance (MR) safety: The essentials to patient safety,” Cureus, vol. 15, no. 10, 2023, Art. no. e47345, doi: 10.7759/cureus.47345.
📄 K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proc. 3rd Int. Conf. Learn. Representations, Comput. Biol. Learn. Soc., 2015, pp. 1–14.
📄 S. A. Sivaprakasam, S. S. Pandi, S. Varsha, M. Vishnu, and J. Sharath, “Parkinson’s disease detection: VGG-ResNet hybrid approach,” in Proc. 10th Int. Conf. Commun. Signal Process., 2024, pp. 54–59, doi: 10.1109/ICCSP60870.2024.10543805.
📄 T.-A. Song et al., “Graph convolutional neural networks for Alzheimer’s disease classification,” in Proc. IEEE 16th Int. Symp. Biomed. Imag., 2019, pp. 414–417, doi: 10.1109/ISBI.2019.8759531.
📄 Y. Uchiyama et al., “Automated classification of cerebral arteries in MRA images and its application to maximum intensity projection,” in Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 2006, pp. 4865–4868, doi: 10.1109/IEMBS.2006.260438.
📄 I. N. Vos et al., “Anatomical markers associated with the presence of intracranial aneurysms in individuals screened for aneurysms,” Stroke, Vasc. Interventional Neurol., vol. 4, no. 4, 2024, Art. no. e001299, doi: 10.1161/SVIN.124.001299.
📄 I. Vos et al., “Data of the circle of willis intracranial artery classification and quantification (CROWN) challenge,” DataverseNL, V2, 2023, doi: 10.34894/R05G1L.
📄 I. N. Vos et al., “Evaluation of techniques for automated classification and artery quantification of the circle of willis on TOF-MRA images: The CROWN challenge,” Med. Image Anal., vol. 105, 2025, Art. no. 103650, doi: 10.1016/j.media.2025.103650.
📄 Z. Vrselja, H. Brkic, S. Mrdenovic, R. Radic, and G. Curic, “Function of circle of willis,” J. Cereb. Blood Flow Metab., vol. 34, no. 4, pp. 578–584, 2014, doi: 10.1038/jcbfm.2014.7.
📄 L. Zhang et al., “An improved MeshCNN with active learning for anatomical labeling of the circle of willis,” in Proc. Int. Conf. Virtual Reality Visual., 2020, pp. 154–158, doi: 10.1109/ICVRV51359.2020.00040.
📄 X. Zhang et al., “Brain age prediction using interpretable multi-feature-based convolutional neural network in mild traumatic brain injury,” NeuroImage, vol. 297, 2024, Art. no. 120751, doi: 10.1016/j.neuroimage.2024.120751.

Most read articles by the same author(s)

1 2 3 > >> 

Similar Articles

1-10 of 21

You may also start an advanced similarity search for this article.