Retinal damar segmantasyonuna yönelik yapay arı koloni algoritması tabanlı yaklaşımların performans mukayesesi

Yıl 2021, , 792 - 807, 04.07.2021

Mehmet Celalettin Cihan Mehmet Bahadır Çetinkaya Hakan Duran

https://doi.org/10.25092/baunfbed.938412

Cited By: 1

Öz

Retinal kan damarlarında meydana gelen yapısal değişiklikler retinal hastalıklara yönelik önemli bilgiler sağlamaktadır. Bu nedenle, son yıllarda bilgisayar destekli retinal damar segmantasyonu uygulamaları önemli bir araştırma alanı haline gelmiştir. Retinal kan damarları ile retina görüntüsü art alanı arasındaki kontrast farkları çok düşük olduğu için retinal kan damarlarının yüksek doğrulukta tespit edilmesine yönelik güçlü algoritmalara ihtiyaç duyulmaktadır. Bu çalışmada, mühendislik problemlerine etkim çözümler üreten yapay arı koloni (ABC) algoritması retinal damar segmantasyonuna yönelik uygulanmıştır. Retinal kan damarlarının yüksek doğrulukta segmantasyonuna yönelik olarak kümeleme tabanlı ABC (temel ABC), hızlı-ABC (Q-ABC) ve modifiye edilmiş ABC (MR-ABC) algoritmaları geliştirilmiş ve performansları mukayese edilmiştir. Benzetimler, DRIVE veri tabanından alınmış olan normal ve hastalıklı retinal görüntüler üzerinde gerçekleştirilmiştir. Benzetim sonuçları ve istatistiksel analizler ABC tabanlı yaklaşımların kararlı bir şekilde çalıştıklarını ve en uygun kümeleme performanslarına yüksek yakınsama hızlarında ulaştıklarını göstermektedir. Sonuç olarak, ABC tabanlı yaklaşımların retinal kan damarlarının yüksek doğrulukta segmantasyonuna yönelik olarak başarılı bir şekilde kullanılabileceği görülmüştür.

Anahtar Kelimeler

Retinal kan damar segmentasyonu, yapay arı koloni algoritması, hızlı yapay arı koloni algoritması, modifiye edilmiş yapay arı koloni algoritması.

Performance comparison of artificial bee colony algorithm based approaches for retinal vessel segmentation

Öz

Structural changes in the retinal blood vessels provide important information about retinal diseases. Therefore, computer-aided segmentation of retinal blood vessels has become an active area of research in last decades. Due to the close contrast between the retinal blood vessels and the retinal background, robust methods should be developed to detect retinal blood vessels with high accuracy. In this work, artificial bee colony (ABC) algorithm which provides effective solutions to engineering problems has been applied to the retinal vessel segmentation. Clustering based ABC (basic ABC), quick-ABC (Q-ABC) and modified ABC (MR-ABC) algorithms have been analyzed for accurate segmentation of retinal blood vessels and their performances were compared. The simulations have been realized on the normal and abnormal retinal images taken from the DRIVE database. Simulation results and statistical analyses represent that ABC based approaches are stable and able to reach to optimal clustering performance with higher convergence rates. As a result it can be concluded that ABC based approaches can successfully be used for accurate segmentation of retinal blood vessels.

Anahtar Kelimeler

Retinal blood vessel segmentation, artificial bee colony algorithm, quick artificial bee colony algorithm, modified artificial bee colony algorithm.

Kaynakça

• Uyen, T.V., Nguyen, A.B., Laurence, A.F.P. and Kotagiri, R., An effective retinal blood vessel segmentation method using multi-scale line detection, Pattern Recognition, 46, 3, 703–715, (2013).
• Shuangling, W., Yilong, Y., Guibao, C., Benzheng, W., Yuanjie, Z. and Gongping, Y., Hierarchical retinal blood vessel segmentation based on feature and ensemble learning, Neurocomputing, 149, Part B, 708–717, (2015).
• Soares, J.V.B., Leandro, J.J.G., Cesar, J.R.M., Jelinek, H.F. and Cree, M.J., Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification, IEEE Medical Imaging, 25, 9, 1214–1222, (2006).
• Frame, A.J., Undrill, P.E., Cree, M.J., Olson, J.A., McHardy, K.C., Sharp, P.F., et al., A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms, Computers in Biology and Medicine, 28, 3, 225–238, (1998).
• Larsen, M., Godt, J., Larsen, N., Lund-Andersen, H., Sjølie, A.K., Agardh, E., et al., Automated detection of fundus photographic red lesions in diabetic retinopathy, Invest Ophthalmol Visual Science, 44, 2, 761–766, (2003).
• Zana, F. and Klein, J.C., Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation, IEEE Transaction on Image Processing, 10, 7, 1010–1019, (2001).
• Jiang, X. and Mojon, D., Adaptive local thresholding by verification based multi threshold probing with application to vessel detection in retinal images, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 1, 131–137, (2003).
• Mendonca, A.M. and Campilho, A., Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction, IEEE Transactions on Medical Imaging, 25, 9, 1200–1213, (2006).
• Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M. and Goldbaum, M., Detection of blood vessels in retinal images using two-dimensional matched filters, IEEE Transactions on Medical Imaging, 8, 3, 263–269, (1989).
• Hoover, A., Kouznetsova, V. and Goldbaum, M., Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response, IEEE Transactions on Medical Imaging, 19, 3, 203–210, (2000).
• Ng, J., Clay, S.T., Barman, S.A., Fielder, A.R., Moseley, M.J., Parker, K.H. and Paterson, C., Maximum likelihood estimation of vessel parameters from scale space analysis, Image and Vision Computing, 28, 1, 55–63, (2010).
• Zhang, B., Zhang, L. and Karray, F., Retinal vessel extraction by matched filter with first-order derivative of Gaussian, Computers in Biology and Medicine, 40, 4, 438–445, (2010).
• Narasimha-Iyer, H., Mahadevan, V., Beach, J.M. and Roysam, B., Improved detection of the central reflex in retinal vessels using a generalized dual-Gaussian model and robust hypothesis testing, IEEE Transactions on Information Technology in Biomedicine, 12, 3, 406–410, (2008).
• Bankhead, P., Scholfield, C.N., McGeown, J.G. and Curties, T.M., Fast retinal vessel detection and measurement using wavelets and edge location refinement, PLoS One, 7, 3, e32435, (2012).
• Zhou, L., Rzeszotarsk, M.S., Singerman, L.J. and Chokreff, J.M., The detection and quantification of retinopathy using digital angiograms, IEEE Transactions on Medical Imaging, 13, 4, 619–626, (1994).
• Delibasis, K.K., Kechriniotis, A.I., Tsonos, C. and Assimakis, N., Automatic model based tracing algorithm for vessel segmentation and diameter estimation, Computational Methods and Programs in Biomedicine, 100, 2, 108-122, (2010).
• Adel, M., Moussaoui, A., Rasigni, M., Bourennane, S. and Hamami, L., Statistical-based tracking technique for linear structures detection: application to vessel segmentation in medical images, IEEE Signal Processing Letters, 17, 6, 555–558, (2010).
• Vlachos, M. and Dermatas, E., Multi-scale retinal vessel segmentation using line tracking, Computerized Medical Imaging and Graphics, 34, 3, 213–227, (2010).
• Perfetti, R., Ricci, E., Casali, D. and Costantini, G., Cellular neural networks with virtual template expansion for retinal vessel segmentation, IEEE Transactions on Circuits and Systems II, 54, 2, 141–145, (2007).
• Fathi, A. and Naghsh-Nilchi, A.R., General rotation-invariant local binary patterns operator with application to blood vessel detection in retinal images, Pattern Analysis and Applications, 17, 1, 69-81, (2014).
• Marín, D., Aquino, A., Gegúndez-Arias, M.E. and Bravo, J.M., A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features, IEEE Transactions on Medical Imaging, 30, 1, 146-158, (2011).
• Staal, J.J., Abramoff, M.D., Niemeijer, M., Viergever, M.A. and VanGinneken, B., Ridge based vessel segmentation in color images of the retina, IEEE Transactions on Medical Imaging, 23, 4, 501–509, (2004).
• Ricci, E. and Perfetti, R., Retinal blood vessel segmentation using line operators and support vector classification, IEEE Transactions on Medical Imaging, 26, 10, 1357–1365, (2007).
• You, X., Peng, Q., Yaun, Y., Cheng, Y. and Lei, J., Segmentation of retinal blood vessels using the radial projection and semi-supervised approach, Pattern Recognition, 44, (11-10), 2314-2324, (2011).
• Garg, S., Sivaswamy, J. and Chandra, S., Unsupervised curvature-based retinal vessel segmentation, Proceedings of the IEEE International Symposium on Bio-Medical Imaging: From Nano to Macro, 344–347, Hyderabad, (2007).
• Palomera-Perez, M.A., Martinez-Perez, M.E., Benitez-Perez, H. and Ortega-Arjona, J.L, Parallel multiscale feature extraction and region growing: application in retinal blood vessel detection, IEEE Transactions on Information Technology in Biomedicine, 14, 2, 500–506, (2010).
• Martinez-Perez, M.E., Hughes, A.D., Thom, S.A., Bharath, A.A. and Parker, K.H., Segmentation of blood vessels from red-free and fluorescein retinal images, Medical Image Analysis, 11, 1, 47–61, (2007).
• Xu, X., Niemeijer, M., Song, Q., Sonka, M., Garvin, M.K., Reinhardt, J.M., et al., Vessel boundary delineation on fundus images using graph based approach, IEEE Transactions on Medical Imaging, 30, 6, 1184–1191, (2011).
• Walter, T., Massin, P., Erginay, A., Ordonez, R., Jeulin, C. and Klein, J.C., Automatic detection of microaneurysms in color fundus images, Medical Image Analysis, 11, 6, 555-566, (2007).
• Hassanien, A.E., Emary, E. and Zawbaa, H.M., Retinal blood vessel localization approach based on bee colony swarm optimization, fuzzy c-means and pattern search, Journal of Visual Communication and Image Representation, 31, 186-196, (2015).
• Karaboga, D., An idea based on honey bee swar for numerical optimization, Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, Kayseri, (2005).
• Karaboga, D. and Gorkemli, B., A quick artificial bee colony (qABC) algorithm and its performance on optimization problems, Applied Soft Computing, 23, 227–238, (2014).
• Karaboga, D. and Basturk, B., Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems, LNCS: Advances in Soft Computing: Foundations of Fuzzy Logic and Soft Computing, 4529,789-798, (2007).
• You X, Peng Q, Yaun Y, Cheng Y, Lei J. Segmentation of retinal blood vessels using the radial projection and semi-supervised approach, Pattern Recognition, 44, 10, 2314-2324, (2011).
• Imani E, Javidi M, Pourreza HR. Improvement of retinal blood vessel detection using morphological component analysis, Computer Methods and Programs in Biomed, 118, 3, 263–279, (2015).
• Mendonca AM, Campilho A. Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction, IEEE Transactions on Medical Imaging, 25, 9, 1200–1213, (2006).
• Azzopardi G, Strisciuglio N, Vento M, Petkov N. Trainable COSFIRE filters for vessel delineation with application to retinal images, Medical Image Analysis, 19, 1, 46-57, (2015).