An Effective Vessel-Precise Frisk Sequence Convolutional Network for Blood Vessel Separation
Keywords:
Vessel separation, DR, CAD, V layerAbstract
Studying vascular pictures, which provide insight into the morphological alterations that have taken place, is the only way to gain an understanding of the diseases that are at the root of the problem. The separation of vessel morphology is the most important phase in the process of analysing vascular images. As a consequence of this, the authors of this study demonstrate the enhancement and separation of blood arteries for images that were acquired from a wide variety of medical imaging modalities. Following the completion of image-specific pre-processing, a VSSC Net is constructed in order to isolate the images of the retinal fundus and CA in order to isolate the blood vessels. The two VE layers that are superimposed on top of VGG-16 are made up of the VS blocks, the SC layers, and the feature map summation with enhanced supervision. It is possible to distinguish the trained vessels from the rest of the image by using a technique known as feature map summation. The effectiveness of this network and the speed at which it can execute were unaffected across all datasets.
Downloads
References
Adams, H. P. and Biller, J. (2015), ‘Classification of Subtypes of Ischemic Stroke’, Stroke 46(5), e114–e117.
Al-Bander, B., Williams, B. M., Al-Nuaimy, W., Al-Taee, M. A., Pratt, H. and Zheng, Y. (2018), ‘Dense fully convolutional separation of the optic disc and cup in color fundus for glaucoma diagnosis’, Symmetry 10(4), 87.
Almotiri, J., Elleithy, K. and Elleithy, A. (2018), ‘Retinal Vessels Separation Techniques and Algorithms: A Survey’, Applied Sciences 8(2), 155.
Aslani, S. and Sarnel, H. (2016), ‘A new supervised retinal vessel separation method based on robust hybrid features’, Biomedical Signal Processing and Control 30, 1–12.
Badsha, S., Reza, A. W., Tan, K. G. and Dimyati, K. (2013), ‘A new blood vessel extraction technique using edge enhancement and object classification’, Journal of Digital Imaging 26(6), 1107–1115.
Bhargava, S. and Somkuwar, A. (2015), ‘Evaluation of noise exclusion of medical images using hybridization of particle swarm optimization and bivariate shrinkage methods’, International Journal of Electrical and Computer Engineering 5(3), 421–428.
Bharkad, S. (2017), ‘Morphological statistical features for automatic separation of blood vessel structure in retinal images, International Journal of Telemedicine and Clinical Practices 2(3), 197.
Carballal, A., Novoa, F. J., Fernandez-Lozano, C., Garc ́ıa-Guimaraes, M., Aldama-Lopez, G., Calvi ́ no-Santos, R., Vazquez-Rodriguez, J. M. and Pazos, A. (2018), ‘Automatic multiscale vascular image separation algorithm for coronary angiography’, Biomedical Signal Processing and Control 46, 1–9.
Castro, V. M., Dligach, D., Finan, S., Yu, S., Can, A., Abd-El-Barr, M., Gainer, V., Shadick, N. A., Murphy, S., Cai, T., Savova, G., Weiss, S. T. and Du, R. (2017), ‘Large-scale identification of patients with cerebral aneurysms using natural language processing, Neurology 88(2), 164–168.
Cervantes-Sanchez, F., Cruz-Aceves, I., Hernandez-Aguirre, A., Hernandez-Gonzalez, M. A. and Solorio-Meza, S. E. (2019), ‘Automatic separation of coronary arteries in X-ray, angiograms using multiscale analysis and artificial neural networks, Applied Sciences 9(24), 5507.
A skeleton context-aware 3D fully convolutional network for abdominal artery separation. Zhu R, Oda M, Hayashi Y, Kitasaka T, Misawa K, Fujiwara M, Mori K.Int J Comput Assist Radiol Surg. 2023 Mar;18(3):461-472. doi: 10.1007/s11548-022-02767-0. Epub 2022 Oct 22.PMID: 36273078
MHA-Net: A Multibranch Hybrid Attention Network for Medical Image Separation. Zhang M, Sun Q, Cai F, Yang C.Comput Math Methods Med. 2022 Oct 6;2022:8375981. doi: 10.1155/2022/8375981. eCollection 2022.PMID: 36245836.
Generative Adversarial Network Combined with SE-ResNet and Dilated Inception Block for Segmenting Retinal Vessels. Yue C, Ye M, Wang P, Huang D, Lu X.Comput Intell Neurosci. 2022 Aug 28;2022:3585506. doi: 10.1155/2022/3585506. eCollection 2022.PMID: 36072751.
A Lightweight Network for Accurate Coronary Artery Separation Using X-Ray Angiograms. Tao X, Dang H, Zhou X, Xu X, Xiong D.Front Public Health. 2022 May 25;10:892418. doi: 10.3389/fpubh.2022.892418. eCollection 2022.PMID: 35692314.
Diabetic and Hypertensive Retinopathy Screening in Fundus Images Using Artificially Intelligent Shallow Architectures. Arsalan M, Haider A, Choi J, Park KR.J Pers Med. 2021 Dec 23;12(1):7. doi: 10.3390/jpm12010007.PMID: 35055322.
Zhang H, Mu L, Hu S, Nallamothu BK, Lansky AJ, Xu B, Bouras G, Cohen DJ, Spertus JA, Masoudi FA, et al. Comparison of physician visual assessment with quantitative coronary angiography in the assessment of stenosis severity in china. JAMA Intern Med. 2018;178(2):239–47.
Cruz-Aceves I, Oloumi F, Rangayyan RM, Avina-Cervantes JG, Hernandez-Aguirre A. Automatic separation of coronary arteries using gabor filters and thresholding based on multiobjective optimization. Biomed Signal Process Control. 2016;25:76–85.
Fazlali HR, Karimi N, Soroushmehr SR, Shirani S, Nallamothu BK, Ward KR, Samavi S, Najarian K. Vessel separation and catheter detection in X-ray angiograms using superpixels. Med Biol Eng Comput. 2018;56(9):1515–30.
Chalakkal, R. J., Abdulla, W. H. and Hong, S. C. (2020), Fundus retinal image analysis for screening and diagnosing diabetic retinopathy, macular edema, and glaucoma disorders, in ‘Diabetes and Fundus OCT’, pp. 59–111.
Chen, S. and Zhao, F. (2018), ‘The Adaptive Fractional Order Differential Model for Image Enhancement Based on Separation’, International Journal of Pattern Recognition and Artificial Intelligence 32(3), 1854005.
Chen, Y. (2017), ‘A labeling-free approach to supervising deep neural networks for retinal blood vessel separation’, arXiv preprint arXiv:1704.07502.
Cruz-Aceves, I., Cervantes-Sanchez, F. and Avila-Garcia, M. S. (2018), ‘A Novel Multiscale Gaussian-Matched Filter Using Neural Networks for the Separation of X-Ray Coronary Angiograms’, Journal of healthcare engineering 2018.
Rajendra, K. ., Subramanian, S. ., Karthik, N. ., Naveenkumar, K. ., & Ganesan, S. . (2023). Grey Wolf Optimizer and Cuckoo Search Algorithm for Electric Power System State Estimation with Load Uncertainty and False Data. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 59–67. https://doi.org/10.17762/ijritcc.v11i2s.6029
Dhablia, A. (2021). Integrated Sentimental Analysis with Machine Learning Model to Evaluate the Review of Viewers. Machine Learning Applications in Engineering Education and Management, 1(2), 07–12. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/12
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
