Review
Year 2017,
Volume: 9 Issue: 4, 198 - 202, 01.12.2017
Abstract
The first step in diagnosing the
disease from retinal images is the segmentation of blood vessels. In this
study, it was aimed to investigate the extraction of blood vessels from retinal
images. For this reason, existing articles in the literature have been compiled
systematically, focusing on the identification of the methods used. Starting
from the first study in the literatüre about this problem, solutions to the
problem of vessel segmentation and studies until recently have been evaluated
within the framework of some criteria. It can be concluded from this review, significant
progress has been made in the methods used for segmentation over the years and segmentation
of all vessels from retinal images has been made easily.
References
- [1] S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, M. Goldbaum, Detection of blood vessels in retinal images using two-dimensional matched filters, IEEE Trans. Med. Imaging 8 (1989) 263–269. [2] T. Chanwimaluang, G. Fan, An efficient algorithm for extraction of anatomical structures in retinal images, in: Proceeding of the IEEE International Conference on Image Processing (ICIP), Spain, 2003, pp. 1193–1196. [3] A. Hoover, V. Kouznetsova, M. Goldbaum, Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response, IEEE Trans. Med. Imaging 19 (2000) 203–210. [4] M.G. Cinsdikici, D. Aydin, Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm, Comp. Methods Prog. Biomed. 96 (2009) 85–95. [5] M. Al-Rawi, M. Qutaishat, M. Arrar, An improved matched filter for blood vessel detection of digital retinal images, Comp. Biol. Med. 37 (2007) 262–267. [6] A.M. Mendonça, A. Campilho, Segmentation of retinal blood vessels bycombining the detection of centerlines and morphological reconstruction,IEEE Trans. Med. Imaging 25 (2006) 1200–1213. [7] F. Zana, J.C. Klein, Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation, IEEE Trans. Image Process. 10 (2001)1010–1019. [8] M. Niemeijer, J.J. Staal, B. van Ginneken, M. Loog, M.D. Abramoff, Comparative study of retinal vessel segmentation methods on a new publicly available database, SPIE Med. Imag. 53 (2004) 648–656. [9] J. Staal, M.D. Abràmoff, M. Niemeijer, M.A. Viergever, B. van Ginneken, Ridge based vessel segmentation in color images of the retina, IEEE Trans. Med. Imaging 23 (2004) 501–509. [10] S. Garg, J. Sivaswamy, S. Chandra, Unsupervised curvature-based retinal vessel segmentation, in: Proceeding of the IEEE International Symposium on Bio-Medical Imaging, USA, 2007, pp. 344–347. [11] C. Sinthanayothin, J.F. Boyce, H.L. Cook, T.H. Williamson, Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images, Br. J. Ophthalmol. 83 (1999) 902–910. [12] M.E. Martinez-Perez, A.D. Hughes, S.A. Thom, A.A. Bharath, K.H. Parker,Segmentation of blood vessels from red-free and fluorescein retinal images, Med. Image Anal. 11 (2007) 47–61. [13] Rezaee K., Haddadnia J., Tashk A., “Optimized clinical segmentation of retinal blood vessels by usingcombination of adaptive filtering, fuzzy entropy and skeletonization” Applied Soft Computing 52 (2017) 937–951 [14] Q. Li, B. Feng, L. Xie, P. Liang, H. Zhang, T. Wang, A cross-modality learning approach for vessel segmentation in retinal images, IEEE Trans. Med. Imaging 35 (2016) 109–118. [15] M. Krause, R.M. Alles, B. Burgeth, J. Weickert, Fast retinal vessel analysis, J.Real-Time Image Process. 11 (2016) 413–422. [16] Barkana B.D., Saricicek I., Yildirim B., “Performance analysis of descriptive statistical features in retinal vessel segmentation via fuzzy logic, ANN, SVM, and classifier fusion”, Knowledge-Based Systems 118 (2017) 165–176. [17] Javidi M., Pourreza H.R., Harati A., “Vessel segmentation and microaneurysm detection using discriminative dictionary learning and sparse representation”, computer methods and programs in biomedicine 139 (2017) 93–108. [18] Frucci M., Riccio D., Baja G.S., Serino L., “Severe: Segmenting vessels in retina images”, Pattern Recognition Letters 82 (2016) 162–169. [19] GeethaRamani R., Balasubramanian L.,” Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis”, biocybernetics and biomedical engineering 36 (2016) 102–118 [20] [1] J.J. Kanski, Clinical Ophthalmology, 6th ed., Elsevier Health Sciences, London, UK, 2007.
Year 2017,
Volume: 9 Issue: 4, 198 - 202, 01.12.2017
Abstract
Retina görüntülerinden hastalık
teşhisinin yapılabilmesinin ilk adımı kan damarlarının segmente edilmesidir. Bu
çalışmada retina görüntüleri üzerinden kan damarlarının çıkartılması üzerine
yapılan çalışmaları incelemeyi amaçlamaktadır. Bu nedenle literatürdeki mevcut
makaleler kullanılan yöntemleri belirlemeye odaklanarak sistematik olarak
derlenmiştir. Damar segmentasyonu problemine çözüm getiren ve literatürde bu
alandaki ilk çalışmadan başlayarak son zamanlara kadar yapılan çalışmalardaki
çözümler bazı kriterler dahilinde değerlendirilmiştir. Bu derleme çalışmasından
anlaşılıyor ki, yıllar içerisinde segmentasyon için kullanılan yöntemlerde
ciddi bir ilerleme kaydedilmiş ve retina görüntülerinden tüm damarların
segmentasyonu kolaylıkla yapılabilir düzeye gelmiştir.
References
- [1] S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, M. Goldbaum, Detection of blood vessels in retinal images using two-dimensional matched filters, IEEE Trans. Med. Imaging 8 (1989) 263–269. [2] T. Chanwimaluang, G. Fan, An efficient algorithm for extraction of anatomical structures in retinal images, in: Proceeding of the IEEE International Conference on Image Processing (ICIP), Spain, 2003, pp. 1193–1196. [3] A. Hoover, V. Kouznetsova, M. Goldbaum, Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response, IEEE Trans. Med. Imaging 19 (2000) 203–210. [4] M.G. Cinsdikici, D. Aydin, Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm, Comp. Methods Prog. Biomed. 96 (2009) 85–95. [5] M. Al-Rawi, M. Qutaishat, M. Arrar, An improved matched filter for blood vessel detection of digital retinal images, Comp. Biol. Med. 37 (2007) 262–267. [6] A.M. Mendonça, A. Campilho, Segmentation of retinal blood vessels bycombining the detection of centerlines and morphological reconstruction,IEEE Trans. Med. Imaging 25 (2006) 1200–1213. [7] F. Zana, J.C. Klein, Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation, IEEE Trans. Image Process. 10 (2001)1010–1019. [8] M. Niemeijer, J.J. Staal, B. van Ginneken, M. Loog, M.D. Abramoff, Comparative study of retinal vessel segmentation methods on a new publicly available database, SPIE Med. Imag. 53 (2004) 648–656. [9] J. Staal, M.D. Abràmoff, M. Niemeijer, M.A. Viergever, B. van Ginneken, Ridge based vessel segmentation in color images of the retina, IEEE Trans. Med. Imaging 23 (2004) 501–509. [10] S. Garg, J. Sivaswamy, S. Chandra, Unsupervised curvature-based retinal vessel segmentation, in: Proceeding of the IEEE International Symposium on Bio-Medical Imaging, USA, 2007, pp. 344–347. [11] C. Sinthanayothin, J.F. Boyce, H.L. Cook, T.H. Williamson, Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images, Br. J. Ophthalmol. 83 (1999) 902–910. [12] M.E. Martinez-Perez, A.D. Hughes, S.A. Thom, A.A. Bharath, K.H. Parker,Segmentation of blood vessels from red-free and fluorescein retinal images, Med. Image Anal. 11 (2007) 47–61. [13] Rezaee K., Haddadnia J., Tashk A., “Optimized clinical segmentation of retinal blood vessels by usingcombination of adaptive filtering, fuzzy entropy and skeletonization” Applied Soft Computing 52 (2017) 937–951 [14] Q. Li, B. Feng, L. Xie, P. Liang, H. Zhang, T. Wang, A cross-modality learning approach for vessel segmentation in retinal images, IEEE Trans. Med. Imaging 35 (2016) 109–118. [15] M. Krause, R.M. Alles, B. Burgeth, J. Weickert, Fast retinal vessel analysis, J.Real-Time Image Process. 11 (2016) 413–422. [16] Barkana B.D., Saricicek I., Yildirim B., “Performance analysis of descriptive statistical features in retinal vessel segmentation via fuzzy logic, ANN, SVM, and classifier fusion”, Knowledge-Based Systems 118 (2017) 165–176. [17] Javidi M., Pourreza H.R., Harati A., “Vessel segmentation and microaneurysm detection using discriminative dictionary learning and sparse representation”, computer methods and programs in biomedicine 139 (2017) 93–108. [18] Frucci M., Riccio D., Baja G.S., Serino L., “Severe: Segmenting vessels in retina images”, Pattern Recognition Letters 82 (2016) 162–169. [19] GeethaRamani R., Balasubramanian L.,” Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis”, biocybernetics and biomedical engineering 36 (2016) 102–118 [20] [1] J.J. Kanski, Clinical Ophthalmology, 6th ed., Elsevier Health Sciences, London, UK, 2007.
There are 1 citations in total.
Details
| Subjects | Health Care Administration |
|---|---|
| Journal Section | Review |
| Authors | |
| Publication Date | December 1, 2017 |
| Published in Issue | Year 2017 Volume: 9 Issue: 4 |
e-ISSN: 2548-0251
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