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Dergiler / International Journal of Advances in Engineering and Pure Sciences / 2026 / Cilt: 30 Sayı: 2

Retina Fundus Floresan Anjiyografi Görüntülerinde Drüsen Alanlarının Otomatik Tespiti ve Büyüklüklerinin Hesaplanması

Automatic Detection And Calculation Of Drusen Areas In Retinal Fundus Fluorescein Angiography Images

Önder Demir(MARMARA ÜNİVERSİTESİ)0000-0002-4894-0005
Buket Doğan(MARMARA ÜNİVERSİTESİ)0000-0003-1062-2439
Esra Çalık Bayezit(FATİH SULTAN MEHMET VAKIF ÜNİVERSİTESİ)0000-0002-7053-3027
Kazım Yıldız(MARMARA ÜNİVERSİTESİ)0000-0001-6999-1410
Sayfa
126–132
Yayın Tarihi
2018-06-30
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Geliştirilen BDT sistemi 66 doğru, 9 hatalı tespit yaparak %88 doğruluk oranı sağlamıştır.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Bilgisayar destekli tespit (BDT) sistemleri biyomedikal görüntülerin analizinde sıklıkla kullanılmaktadır.
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Temel Bulgular & Veri:Geliştirilen BDT sistemi 4 aşamadan oluşmaktadır, a) önişleme aşaması, b) bölütleme aşaması, c) ilgi alanı tespiti ve d) öznitelik çıkarma ve tespit aşaması.
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Sonuç & Katkı:Geliştirilen BDT sistemi 66 doğru, 9 hatalı tespit yaparak %88 doğruluk oranı sağlamıştır.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Bigisayar Destekli Tespit · Yaşa Bağlı Makula Dejenerasyonu · Morfolojk Görüntü İşleme

Özet

Bilgisayar destekli tespit (BDT) sistemleri biyomedikal görüntülerin analizinde sıklıkla kullanılmaktadır. Bu çalışmada retinal fundus anjiyografi görüntüleri üzerinde yaşa bağlı makula dejenerasyonu (YBMD) hastalığının tespiti için bir BDT sistemi gerçekleştirilmiş ve patojenik drusen alanlarının büyüklüğünün hesaplanması sağlanmıştır. Çalışmanın amacı YBMD hastalığının görüldüğü alanların tespitinin ve büyüklüğünü hesaplamanın yanında hastalığa karşı uygulanan tedavinin sonucunun takibini de sağlamaktır. Geliştirlen sistemin yardımıyla optalmoloji uzmanları işaretlenen alanları kısa sürede tespit edebilirler ve hastalığın tedaviye verdiği cevabı basit bir şekilde gözlemleyebileceklerdir. Geliştirilen BDT sistemi 4 aşamadan oluşmaktadır, a) önişleme aşaması, b) bölütleme aşaması, c) ilgi alanı tespiti ve d) öznitelik çıkarma ve tespit aşaması. Geliştirlen BDT sistemi 75 görüntüden oluşan bir verisetiyle test edilmiştir. BST sisteminin elde ettiği sonuçlar bir optalmoloji uzmanıyla karşılaştırılarak sonuç bölümünde sunulmuştur. Geliştirilen BDT sistemi 66 doğru, 9 hatalı tespit yaparak %88 doğruluk oranı sağlamıştır.

Abstract

Computer aided detection (CAD) systems are widely used in the analysis of biomedical images. In this paper, we present a novel CAD system to detect age-related macular degeneration (ARMD) on retinal fundus fluorescein angiography (FFA) images, and we provide an areal size calculation of pathogenic drusen regions. The purpose of this study is to enable identification and areal size calculation of ARMD-affected regions with the developed CAD system; hence, we aim to discover the condition of the disease as well as facilitate long-term patient follow-up treatment. With the aid of this system, assessing the marked regions will take less time for ophthalmologists and observing the progress of the treatment will be a simpler process. The CAD system consists of four stages, a) preprocessing stage, b) segmentation stage, c) region of interest detection and d)feature extraction stage and detection stage. Detection through CAD and calculation of drusen regions were performed with a dataset composed of 75 images. The results obtained from the developed CAD system were examined by a specialist ophthalmologist, and the performance criteria of the CAD system are reported as conclusions. As a result, with 66 correct detections and 9 incorrect detections, the developed CAD system achieved an accuracy rate of 88%.

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Bu Makaleye Atıf Yap

Demir, Ö., Doğan, B., Bayezit, E. Ç., Yıldız, K. (2018). Retina Fundus Floresan Anjiyografi Görüntülerinde Drüsen Alanlarının Otomatik Tespiti ve Büyüklüklerinin Hesaplanması. International Journal of Advances in Engineering and Pure Sciences, Cilt: 30 Sayı: 2, 126–132. https://doi.org/10.7240/marufbd.356425

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Kaynakça (27)

  1. Edwards, M., Bressler, N., & Raja, S. (1999). Macular disorders-Age-related macular degeneration. Ophthalmology, 1st ed. London, UK: Mosby International Kılınç, D., Borandağ, E., Yücalar, F., Tunalı, V., Şimsek, M., & Özçift, A. (2016). KNN algoritması ve r dili ile metin madenciliği kullanılarak bilimsel makale tasnifi. Coleman, H. R., Chan, C.-C., Ferris, F. L., & Chew, E. Y. (2008). Age-related macular degeneration. The Lancet, 372(9652), 1835-1845.
  2. Ding, X., Patel, M., & Chan, C.-C. (2009). Molecular pathology of age-related macular degeneration. Progress in retinal and eye research, 28(1), 1-18.
  3. Novotny, H. R., & Alvis, D. L. (1961). A method of photographing fluorescence in circulating blood in the human retina. Circulation, 24(1), 82-86.
  4. Kwan, A. S., Barry, C., McAllister, I. L., & Constable, I. (2006). Fluorescein angiography and adverse drug reactions revisited: the Lions Eye experience. Clinical & Experimental ophthalmology, 34(1), 33-38.
  5. Geng, L., Shao, Y.-T., Xiao, Z.-T., Zhang, F., Wu, J., Li, M., & Shan, C.-Y. (2014). Fundus optic disc localization and segmentation method based on phase congruency. Bio-medical materials and engineering, 24(6), 3223-3229.
  6. Pourreza, R., Pourreza, H., & Banaee, T. (2010). Segmentation of blood vessels in fundus color images by Radon transform and morphological reconstruction. Paper presented at the Advanced Computational Intelligence (IWACI), 2010 Third International Workshop on. Calik, E., Dogan, B., & Ucan, O. N. (2015). Computer Aided Detection of age related macular degeneration in retinal images. Paper presented at the Signal Processing and Communications Applications Conference (SIU), 2015 23th. Soares, J. V., Leandro, J. J., Cesar, R. M., Jelinek, H. F., & Cree, M. J. (2006). Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Transactions on Medical Imaging, 25(9), 1214-1222.
  7. Freund, D. E., Bressler, N., & Burlina, P. (2009). Automated detection of drusen in the macula. Paper presented at the ISBI'09. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009.
  8. Abramoff, M. D., & Niemeijer, M. (2006). The automatic detection of the optic disc location in retinal images using optic disc location regression. Paper presented at the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ yEMBS'06, 2006.
  9. Brandon, L., & Hoover, A. (2003). Drusen detection in a retinal image using multi-level analysis Medical Image Computing and Computer-Assisted Intervention-MICCAI 2003 (pp. 618-625):
  10. Springer. Güven, A. (2013). Automatic detection of age-related macular degeneration pathologies in retinal fundus images. Computer methods in biomechanics and biomedical engineering, 16(4), 425-434.
  11. Yavuz, Z., & Köse, C. (2011). Retinal blood vessel segmentation using Gabor filter and top-hat transform. Paper presented at the Signal Processing and Communications Applications (SIU), 2011 IEEE 19th Conference on. Joseph, M. S. G. (2015). Automated Drusen Detection and Quantification for Early Identification of Age Related Macular Degeneration in Retinal Images Using Analytical Modelling Algorithms. Vingerling, J. R., Dielemans, I., Hofman, A., Grobbee, D. E., Hijmering, M., Kramer, C. F., & de Jong, P. T. (1995). The prevalence of age-related maculopathy in the Rotterdam Study. Ophthalmology, 102(2), 205-210.
  12. Jonasson, F., Arnarsson, A., Sverrisson, T., Stefánsson, E., Sigurdsson, H., Gislason, I.,. Bird, A. (2003). 5-year incidence of age-related maculopathy-Reykjavik Eye study. Investigative Ophthalmology & Visual Science, 44(13), 3083-3083.
  13. McCormick, B., & Goldbaum, M. (1975). STARE= Structured Analysis of the Retina: Image processing of TV fundus image. Paper presented at the Jet Propulsion Laboratory, Pasadena, CA: USA-Japan Workshop on Image Processing. Dougherty, G. (2009). Digital image processing for medical applications: Cambridge University Press.
  14. Dhawan, A. P. (2011). Medical image analysis (Vol. 31): John Wiley & Sons. Edwards, M., Bressler, N., & Raja, S. (1999). Macular disorders-Age-related macular degeneration. Ophthalmology, 1st ed. London, UK: Mosby International Kılınç, D., Borandağ, E., Yücalar, F., Tunalı, V., Şimsek, M., & Özçift, A. (2016). KNN algoritması ve r dili ile metin madenciliği kullanılarak bilimsel makale tasnifi. Coleman, H. R., Chan, C.-C., Ferris, F. L., & Chew, E. Y. (2008). Age-related macular degeneration. The Lancet, 372(9652), 1835-1845.
  15. Ding, X., Patel, M., & Chan, C.-C. (2009). Molecular pathology of age-related macular degeneration. Progress in retinal and eye research, 28(1), 1-18.
  16. Novotny, H. R., & Alvis, D. L. (1961). A method of photographing fluorescence in circulating blood in the human retina. Circulation, 24(1), 82-86.
  17. Kwan, A. S., Barry, C., McAllister, I. L., & Constable, I. (2006). Fluorescein angiography and adverse drug reactions revisited: the Lions Eye experience. Clinical & Experimental ophthalmology, 34(1), 33-38.
  18. Geng, L., Shao, Y.-T., Xiao, Z.-T., Zhang, F., Wu, J., Li, M., & Shan, C.-Y. (2014). Fundus optic disc localization and segmentation method based on phase congruency. Bio-medical materials and engineering, 24(6), 3223-3229.
  19. Pourreza, R., Pourreza, H., & Banaee, T. (2010). Segmentation of blood vessels in fundus color images by Radon transform and morphological reconstruction. Paper presented at the Advanced Computational Intelligence (IWACI), 2010 Third International Workshop on. Calik, E., Dogan, B., & Ucan, O. N. (2015). Computer Aided Detection of age related macular degeneration in retinal images. Paper presented at the Signal Processing and Communications Applications Conference (SIU), 2015 23th. Soares, J. V., Leandro, J. J., Cesar, R. M., Jelinek, H. F., & Cree, M. J. (2006). Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Transactions on Medical Imaging, 25(9), 1214-1222.
  20. Freund, D. E., Bressler, N., & Burlina, P. (2009). Automated detection of drusen in the macula. Paper presented at the ISBI'09. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009.
  21. Abramoff, M. D., & Niemeijer, M. (2006). The automatic detection of the optic disc location in retinal images using optic disc location regression. Paper presented at the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ yEMBS'06, 2006.
  22. Brandon, L., & Hoover, A. (2003). Drusen detection in a retinal image using multi-level analysis Medical Image Computing and Computer-Assisted Intervention-MICCAI 2003 (pp. 618-625):
  23. Springer. Güven, A. (2013). Automatic detection of age-related macular degeneration pathologies in retinal fundus images. Computer methods in biomechanics and biomedical engineering, 16(4), 425-434.
  24. Yavuz, Z., & Köse, C. (2011). Retinal blood vessel segmentation using Gabor filter and top-hat transform. Paper presented at the Signal Processing and Communications Applications (SIU), 2011 IEEE 19th Conference on. Joseph, M. S. G. (2015). Automated Drusen Detection and Quantification for Early Identification of Age Related Macular Degeneration in Retinal Images Using Analytical Modelling Algorithms. Vingerling, J. R., Dielemans, I., Hofman, A., Grobbee, D. E., Hijmering, M., Kramer, C. F., & de Jong, P. T. (1995). The prevalence of age-related maculopathy in the Rotterdam Study. Ophthalmology, 102(2), 205-210.
  25. Jonasson, F., Arnarsson, A., Sverrisson, T., Stefánsson, E., Sigurdsson, H., Gislason, I.,. Bird, A. (2003). 5-year incidence of age-related maculopathy-Reykjavik Eye study. Investigative Ophthalmology & Visual Science, 44(13), 3083-3083.
  26. McCormick, B., & Goldbaum, M. (1975). STARE= Structured Analysis of the Retina: Image processing of TV fundus image. Paper presented at the Jet Propulsion Laboratory, Pasadena, CA: USA-Japan Workshop on Image Processing. Dougherty, G. (2009). Digital image processing for medical applications: Cambridge University Press.
  27. Dhawan, A. P. (2011). Medical image analysis (Vol. 31): John Wiley & Sons.