Evaluation of Edge Detection and Fusion Methods for Angiographic Image Processing
Keywords:
medical image analysis, coronary artery disease, edge detection, image fusion, structural similarity
Abstract
This study explores the application of digital image processing techniques to improve the diagnostic accuracy of coronary artery disease through enhanced analysis of angiographic images. A curated dataset of 40 grayscale, single-vessel angiograms was used to evaluate the effectiveness of various preprocessing and edge detection methods. Preprocessing steps included noise reduction, histogram equalization, and morphological operations aimed at improving image clarity and highlighting vascular structures. Three edge detection algorithms, Otsu, Canny, and Roberts, were applied, and their outputs were further combined using the Dempster-Shafer fusion theory. Performance was assessed using edge-based structural similarity (ESSIM) and the percentage error in vessel diameter measurements. The Canny algorithm demonstrated the highest individual accuracy, while the fusion of Canny and Roberts yielded superior results, achieving the lowest error and the highest similarity index.
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References
[1]. Chellappa R., Rosenfeld A., Image Processing, Encyclopedia of Physical Science and Technology, p. 595-630, 2003.
[2]. Ekstrom M. P., Digital Image Processing Techniques, Academic Press, Orlando, FL, 1984.
[3]. Abràmoff M. D., et al., Pivotal trial of an autonomous AIbased diagnostic system for detection of diabetic retinopathy in primary care offices, Digital Medicine, 1(1), p. 1-8. https://doi.org/10.1038/s41746-018-0040-6, 2018.
[4]. Riaz T., et al., The analysis of retinal blood vessels and systemic diseases includes the relationship between retinal blood vessels and myocardial infarction (heart disease), and retinal blood vessels and cerebrovascular diseases, IAIM, 10(11), p. 61-68, 2023.
[5]. 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, Dec. 2017.
[6]. 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.
[7]. Acharya and Ray, Image Processing Principles and Applications, Wiley-Interscience, ISBN 0-471-71998-6, 2005.
[8]. Buades A., et al., Efficient joint noise removal and multi exposure fusion, PloS One, 17(3), e0265464, doi: 10.1371/journal.pone.0265464, 2022.
[9]. Haddadi Y. R., et al., A novel medical image enhancement algorithm based on CLAHE and pelican optimization, Multimed Tools Appl, https://doi.org/10.1007/s11042-024-19070-6, 2024.
[10]. Russ, The Processing Handbook, Fourth Edition, CRC 2002 ISBN 0-8493-2532-3.
[11]. Hanan Saleh S. Ahmed, J. Nordin, Improving Diagnostic Viewing of Medical Images using Enhancement Algorithms, Journal of Computer Science, 7(12), p. 1831-1838, 2011.
[12]. 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.
[13]. Klein R., et al., Changes in retinal vessel diameter and incidence and progression of diabetic retinopathy, Arch Ophthalmol, 130, p. 749-755, PMID: 22332203, 2012.
[14]. Fielding S., Segmentation and Focus. Digital Video Processing for Engineers, p. 181-190, doi:10.1016/b978-0-12-415760-6.00019-2, 2013.
[15]. Otsu N., A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, p. 62-66, doi: 10.1109/TSMC.1979.4310076, 1979.
[16]. Canny J., A Computational Approach to Edge Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-8, no. 6, p. 679-698, doi: 10.1109/TPAMI.1986.4767851, 1986.
[17]. Mogos G., Quantum Image Processing Using Edge Detection Based on Roberts Cross Operators, Lecture Notes in Networks and Systems, vol 945, Springer, Singapore, https://doi.org/10.1007/978-981-97-1320-2_13, 2024.
[18]. Moraru L., Obreja C. D., 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.
[19]. Qiu S., et al., Fortino, Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges, Information Fusion, vol. 80, p. 241-265, doi.org/10.1016/j.inffus.2021.11.006, 2022.
[20]. Betrabet S., Bhogayta C. K., Structural Similarity based Image Quality Assessment using full reference method, International Journal of Scientific Engineering and Technology, 4(4), p. 252- 255, 2015.
[21]. Li Cui, Alastair R. Allen, An image quality metric based on corner, edge and symmetry maps, In BMVC, p. 1-10, 2008.
[22]. Cheng J., Wang Z., Improved structural similarity index for image quality assessment, Journal of Computer Science, 10(2), p. 353-360, 2014.
[2]. Ekstrom M. P., Digital Image Processing Techniques, Academic Press, Orlando, FL, 1984.
[3]. Abràmoff M. D., et al., Pivotal trial of an autonomous AIbased diagnostic system for detection of diabetic retinopathy in primary care offices, Digital Medicine, 1(1), p. 1-8. https://doi.org/10.1038/s41746-018-0040-6, 2018.
[4]. Riaz T., et al., The analysis of retinal blood vessels and systemic diseases includes the relationship between retinal blood vessels and myocardial infarction (heart disease), and retinal blood vessels and cerebrovascular diseases, IAIM, 10(11), p. 61-68, 2023.
[5]. 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, Dec. 2017.
[6]. 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.
[7]. Acharya and Ray, Image Processing Principles and Applications, Wiley-Interscience, ISBN 0-471-71998-6, 2005.
[8]. Buades A., et al., Efficient joint noise removal and multi exposure fusion, PloS One, 17(3), e0265464, doi: 10.1371/journal.pone.0265464, 2022.
[9]. Haddadi Y. R., et al., A novel medical image enhancement algorithm based on CLAHE and pelican optimization, Multimed Tools Appl, https://doi.org/10.1007/s11042-024-19070-6, 2024.
[10]. Russ, The Processing Handbook, Fourth Edition, CRC 2002 ISBN 0-8493-2532-3.
[11]. Hanan Saleh S. Ahmed, J. Nordin, Improving Diagnostic Viewing of Medical Images using Enhancement Algorithms, Journal of Computer Science, 7(12), p. 1831-1838, 2011.
[12]. 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.
[13]. Klein R., et al., Changes in retinal vessel diameter and incidence and progression of diabetic retinopathy, Arch Ophthalmol, 130, p. 749-755, PMID: 22332203, 2012.
[14]. Fielding S., Segmentation and Focus. Digital Video Processing for Engineers, p. 181-190, doi:10.1016/b978-0-12-415760-6.00019-2, 2013.
[15]. Otsu N., A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, p. 62-66, doi: 10.1109/TSMC.1979.4310076, 1979.
[16]. Canny J., A Computational Approach to Edge Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-8, no. 6, p. 679-698, doi: 10.1109/TPAMI.1986.4767851, 1986.
[17]. Mogos G., Quantum Image Processing Using Edge Detection Based on Roberts Cross Operators, Lecture Notes in Networks and Systems, vol 945, Springer, Singapore, https://doi.org/10.1007/978-981-97-1320-2_13, 2024.
[18]. Moraru L., Obreja C. D., 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.
[19]. Qiu S., et al., Fortino, Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges, Information Fusion, vol. 80, p. 241-265, doi.org/10.1016/j.inffus.2021.11.006, 2022.
[20]. Betrabet S., Bhogayta C. K., Structural Similarity based Image Quality Assessment using full reference method, International Journal of Scientific Engineering and Technology, 4(4), p. 252- 255, 2015.
[21]. Li Cui, Alastair R. Allen, An image quality metric based on corner, edge and symmetry maps, In BMVC, p. 1-10, 2008.
[22]. Cheng J., Wang Z., Improved structural similarity index for image quality assessment, Journal of Computer Science, 10(2), p. 353-360, 2014.
Published
2025-12-15
How to Cite
1.
OBREJA C-D. Evaluation of Edge Detection and Fusion Methods for Angiographic Image Processing. The Annals of “Dunarea de Jos” University of Galati. Fascicle IX, Metallurgy and Materials Science [Internet]. 15Dec.2025 [cited 19Dec.2025];48(4):106-10. Available from: https://gup.ugal.ro/ugaljournals/index.php/mms/article/view/9540
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