Enhanced Retinal Vessel Detection Using Gradient Pyramid Fusion Algorithm
Keywords:
wavelet transform, gradient pyramid fusion, edge detection, image fusion, guided filter
Abstract
The main goal of this study is to create an innovative approach to retinal vessel detection using the gradient pyramid fusion algorithm to improve edge continuity and measurement precision in retinal images. Traditional methods, like wavelet transform and guided filter, face challenges with background noise, artifacts and uneven illumination, which can distort vessel measurement. The proposed fusion method manages to overcome these limitations by combining the strengths of traditional techniques, which creates more continuous edges through gradient fusion across multiple scales, thus managing to also limit the number of image artifacts. We used images from the DRIVE database, to evaluate the fusion algorithm’s precision, with results showing improved vascular tree detection and continuous edges, a reduction in the number of artifacts and improved measurement accuracy. The results show that this image fusion method improves the retinal image analysis, thus helping in early disease diagnosis.
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References
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[2]. Antal B, Hajdu A., An ensemble-based system for microaneurysm detection and diabetic retinopathy grading, IEEE Transactions on Bio-medical Engineering, 59(6), p. 1720-1726, DOI: 10.1109/tbme.2012.2193126, 2012.
[3]. Fraz M. M., et al., Blood vessel segmentation methodologies in retinal images–a survey, Computer Methods and Programs in Biomedicine, 108 (1), p. 407-433, https://doi.org/10.1016/j.cmpb.2012.03.009, 2012.
[4]. Niemeijer M., et al., Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs, IEEE Trans Med Imaging, 29(1), p. 185-95, doi: 10.1109/TMI.2009.2033909, 2010.
[5]. Staal J., et al., Ridge-based vessel segmentation in color images of the retina, IEEE Transactions on Medical Imaging, 23 (4), p. 501-509, https://doi.org/10.1109/TMI.2004.825627, 2004.
[6]. Zana F., Klein J. C., Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation, IEEE Transactions on Image Processing, 10 (7), p. 1010-1019, https://doi.org/10.1109/83.931095, 2001.
[7]. Hoover A., Kouznetsova V., Goldbaum M., Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response, IEEE Transactions on Medical Imaging, 19 (3), p. 203-210, https://doi.org/10.1109/42.845178, 2000.
[8]. Soares J. V., et al., Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification, IEEE Transactions on Medical Imaging, 25 (9), p. 1214-1222, https://doi.org/10.1109/TMI.2006.879967, 2006.
[9]. Yin Y., Adel M., Bourennane S., Retinal vessel segmentation using a probabilistic tracking method, Pattern Recognition, 45 (4), p. 1235-1244, https://doi.org/10.1016/j.patcog.2011.09.019, 2012.
[10]. Kaur H., Koundal D., Kadyan V., Image Fusion Techniques: A Survey, Arch Computat Methods Eng, 28, p. 4425-4447, https://doi.org/10.1007/s11831-021-09540-7, 2021.
[11]. Budai A., et al., Robust vessel segmentation in fundus images, International Journal of Biomedical Imaging, Article 154860, https://doi.org/10.1155/2013/154860, 2013.
[12]. Al-Diri B., Hunter A., Steel D., An active contour model for segmenting and measuring retinal vessels, IEEE Trans Med Imaging, 28(9), p. 1488-97, doi: 10.1109/TMI.2009.2017941, 2009.
[13]. Zhang B., et al., Retinal vessel extraction by matched filter with first-order derivative of Gaussian, Computer Methods and Programs in Biomedicine, 122 (3), p. 168-179.
https://doi.org/10.1016/j.cmpb.2015.08.008, 2015.
[14]. Dashtbozorg B., et al., An automatic graph-based approach for artery/vein classification in retinal images, IEEE Trans Image Process, 23(3), p. 1073-83, doi: 10.1109/TIP.2013.2263809, 2014.
[15]. Zhang L., Zhang J., A New Saliency-Driven Fusion Method Based on Complex Wavelet Transform for Remote Sensing Images, in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 12, p. 2433-2437, doi: 10.1109/LGRS.2017.2768070, 2017.
[16]. Jiang X., Mojon D. S., Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25 (1), p. 131-137, https://doi.org/10.1109/TPAMI.2003.1159954, 2003.
[17]. ***, DRIVE: Digital Retinal Images for Vessel Extraction (Image Sciences Institute, University Medical Center Utrecht), http://www.isi.uu.nl/Research/Databases/DRIVE/, 2004.
[18]. He K., Sun J., Tang X., Guided image filtering, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(6), p. 1397-1409, https://doi.org/10.1109/TPAMI.2012.213, 2013.
[19]. He K., Sun J., Tang X., Guided image filtering, IEEE Trans Pattern Anal Mach Intell, 35(6), p. 1397-409, doi: 10.1109/TPAMI.2012.213, PMID: 23599054, 2013.
[20]. Kou F., et al., Gradient Domain Guided Image Filtering, IEEE Trans Image Process, 24 (11), p. 4528-39, doi: 10.1109/TIP.2015.2468183, 2015.
[21]. Mallat S., A theory for multiresolution signal decomposition: The wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11 (7), p. 674-693, https://doi.org/10.1109/34.192463, 1989.
[22]. Donoho D. L., De-noising by soft-thresholding, IEEE Transactions on Information Theory, 41 (3), p. 613-627, https://doi.org/10.1109/18.382009, 1995.
[23]. Li S., Kang X., Hu J., Image fusion with guided filtering, IEEE Transactions on Image Processing, 22 (7), p. 2864-2875, https://doi.org/10.1109/TIP.2013.2244222, 2013.
[24]. Burt P. J., Adelson E. H., The Laplacian pyramid as a compact image code, IEEE Transactions on Communications, 31 (4), p. 532-540, https://doi.org/10.1109/TCOM.1983.1095851, 1983.
[25]. Shao Z., Cai J., Remote Sensing Image Fusion with Deep Convolutional Neural Network, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 5, p. 1656-1669, doi: 10.1109/JSTARS.2018.2805923, 2018.
[26]. Moraru L., et al., Retinal vessel enhancement based on the Gaussian function and image fusion, AIP Conf. Proc., 1796 (1), 040007, https://doi.org/10.1063/1.4972385, 2017.
[27]. Li R., et al., A survey of multi-source image fusion, Multimed Tools Appl, 83, p. 18573-18605, https://doi.org/10.1007/s11042-023-16071-9, 2024.
Published
2024-12-15
How to Cite
1.
OBREJA C-D. Enhanced Retinal Vessel Detection Using Gradient Pyramid Fusion Algorithm. The Annals of “Dunarea de Jos” University of Galati. Fascicle IX, Metallurgy and Materials Science [Internet]. 15Dec.2024 [cited 4Apr.2025];47(4):32-7. Available from: https://gup.ugal.ro/ugaljournals/index.php/mms/article/view/7493
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