RETINAL VESSELS EXTRACTION BASED ON IMPROVING LINE DETECTION
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Published:
Jul 20, 2023
Keywords:
blood vessel, extraction, line detection, retinal image, winning line
Main Article Content
Abstract
In the medical field, medical images have supported doctors in detecting some dangerous problems related to health. These problems are caused by abnormalities in retinal blood vessels. Therefore, analysis of retinal vascular features is a good way to detect these abnormalities that help doctors make suitable treatment plans in the early stage. One of the retinal analysis tasks is retinal blood vessel extraction which plays an important role because it needs to be done before any measurement can be created. This paper proposed an approach for retinal blood vessel extraction based on multi-scale line detection. In the proposed method, the input image is applied by a multi-scale line detection technique to detect retinal blood vessels. As a result, our method can work effectively to extract more vessels. The performance of the proposed method evaluates both quantitatively and qualitatively on publicly available DRIVE datasets with an accuracy average reaching 96%. The result of the proposed method is better than the other methods.
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1.
Nguyen H. RETINAL VESSELS EXTRACTION BASED ON IMPROVING LINE DETECTION. journal [Internet]. 20Jul.2023 [cited 19May2024];13(6). Available from: https://journal.tvu.edu.vn/index.php/journal/article/view/2105
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References
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[14] Nguyen UTV, Bhuiyan A, Park LAF, Ramamohanarao K. An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern
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[15] Mustafa WA, Mahmud AS, Khairunizam W, Razlan ZM, Shahriman AB, Zunaidi I. Blood vessel extraction using combination OF Kirsch’s templates and fuzzy C-means (FCM) on retinal images. In: IOP Conference Series: Materials Science and Engineering. IOP Publishing; 2019.
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[17] Kaggle.com. Digital retinal images for vessel extraction.
https://www.kaggle.com/datasets/andrewmvd/drivedigital-retinal-images-for-vessel-extraction/code
[Accessed 4th March 2023]
signs as risk indicators of cardiovascular morbidity
and mortality. British Medical Bulletin. 2005;73(1): 57–70.
[2] Sussman EJ, Tsiaras WG, Soper KA. Diagnosis of
diabetic eye disease. The Journal of the American
Medical Association. 1982;247(23): 3231–3234.
[3] Kaur D, Kaur Y. Various image segmentation techniques: A review. International Journal of Computer
Science and Mobile Computing. 2014;3(5): 809–814.
[4] Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi
A. (eds.) Medical Image Computing and ComputerAssisted Intervention – MICCAI 2015. MICCAI 2015.
Cham: Springer; 2015. https://doi.org/10.1007/978-3-
319-24574-4_28.
[5] Al-amri Salem Saleh, Kalyankar NV, Khamitkar SD.
Image segmentation by using thershod techniques.
Journal of Computing. 2010;2: 83–86.
[6] Sujji GE, Lakshmi YVS, Jiji GW. MRI brain image
segmentation based on thresholding. International
Journal of Advanced Computer Research. 2013;3(1):
97–101.
[7] Li L, Verma M, Nakashima Y, Kawasaki R, Nagahara H. Joint learning of vessel segmentation and
artery/vein classification with post-processing. In:
Medical Imaging with Deep Learning, Proceedings
of Machine Learning Research; 2020. p. 440–453.
https://proceedings.mlr.press/v121/li20a.html.
[8] Roth HR, Shen C, Oda H, Oda M, Hayashi Y, Misawa
K, et al. Deep learning and its application to medical
image segmentation. Medical Imaging Technology
Journal. 2018;36(2): 1–6.
[9] Li Q, You J, Zhang D. Vessel segmentation and width
estimation in retinal images using multiscale production of matched filter responses. Expert Systems with
Application. 2012;39(9): 7600–7610.
[10] Ng HP, Ong SH, Foong KWC, Goh PS,
Nowinski WL. Medical image segmentation
using k-means clustering and improved
watershed algorithm. In: 2006 IEEE Southwest
Symposium on Image Analysis and Interpretation,
Denver, CO, USA. IEEE: 2006. p.61–65.
https://doi.org/10.1109/SSIAI.2006.1633722.
[11] Staal J, Abràmoff MD, Niemeije M, Viergever
MA, Ginneken BV. Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging. 2004;23(4): 501–509.
https://doi.org/10.1109/TMI.2004.825627
[12] Soares JV, Leandro JJ, Cesar RM, Jelinek HF, Cree
MJ. Retinal vessel segmentation using the 2-D Gabor
wavelet and supervised classification. IEEE Transactions on Medical Imaging. 2006;25(9): 1214–1222.
[13] Nguyen TB, Vo THT, Nguyen MH, Nguyen TT. Retinal vessels segmentation by improving salient region
combined with Sobel operator condition. In: Dang T,
Kung J, Takizawa M, Bui S. (eds) ¨ Future Data and
Security Engineering: 6th International Conference,
FDSE 2019, Nha Trang City, Vietnam, November
27–29, 2019, Proceedings 6. Springer International
Publishing; 2019. p.608–617.
[14] Nguyen UTV, Bhuiyan A, Park LAF, Ramamohanarao K. An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern
Recognition. 2013;46(3): 703–715.
[15] Mustafa WA, Mahmud AS, Khairunizam W, Razlan ZM, Shahriman AB, Zunaidi I. Blood vessel extraction using combination OF Kirsch’s templates and fuzzy C-means (FCM) on retinal images. In: IOP Conference Series: Materials Science and Engineering. IOP Publishing; 2019.
https://doi.org/10.1088/1757-899X/557/1/012009.
[16] Vlachos M, Dermatas E. Multi-scale retinal vessel
segmentation using line tracking. Computerized Medical Imaging and Graphics. 2010;34(3): 213–227.
[17] Kaggle.com. Digital retinal images for vessel extraction.
https://www.kaggle.com/datasets/andrewmvd/drivedigital-retinal-images-for-vessel-extraction/code
[Accessed 4th March 2023]