Araştırma Makalesi
Comparison of Performances of Different Image Processing Methods in Detection of Prematurity Retinal Blood Vessels
Öz
The properties of the blood vessels in the retina are very important in the diagnosis of retinopathy of prematurity (ROP). In premature infants, the blood vessels of the retina cannot complete their development. Post-natal, certain triggering circumstances cause the creation of regular veins to cease and abnormal blood vessels begin to enlarge forming abnormal tissue. With the increase in the degree of this condition, retinal damage may occur. It is crucial to follow the progress of the disease by following the developments in the vascular networks, especially since babies born prematurely are at greater risk for ROP. The purpose of this study is to develop methods for the detection and segmentation of vascular pathways in ROP images by applying image processing methods to retinal images of preterm neonatal. These methods have been applied to ROP images and the results have been compared numerically. As a result, according to the Peak Signal to Noise Ratio (PSNR) values of the most appropriate image processing method; It has been determined that there is an OTSU filter as the thresholding method and a Gaussian filter as the filtering algorithm.
Anahtar Kelimeler
Kaynakça
- E. Koç, A. Baş, Ş. Özdek ve F. Ovalı. (2021) Türkiye Prematüre Retinopatisi Rehberi 2021 [Online]. Avaiable: https://www.todnet.org/tod-rehber/rop-tedavi-rehberi-2021.pdf
- J. J. Kanski, Clinical Ophthalmology: A Systemic Approach, 6th ed. London, UK: Elsevier Health Sciences, 2007, pp. 952-955.
- F. Zana and J. C. Klein, “Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation,” IEEE Trans. Image Process, 10 (7), 1010–1019, 2001.
- B. Toptaş ve D. Hanbay, “Retina kan damarlarını çıkarmak için eşikleme temelli morfolojik bir yöntem,” NÖHÜ Müh. Bilim. Derg, 11 (1), 010-016, 2022.
- M. Niemeijer, J. Staal, B. Van Ginneken, M. Loog and M. D. Abramoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” In Medical imaging 2004: image processing SPIE, 5370, 648-656, 2004.
- R. GeethaRamani, L. Balasubramanian, “Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis,” Biocybernetics and Biomedical Engineering, 36 (1), 102-118, 2016.
- R. Y. Foos, “Chronic retinopathy of prematurity,” Ophthalmology, 92 (4), 563-74, 1985.
- J. A. Kylstra and T. Wierzbicki, M. L. Wolbarsht,”The relationship between retinal vessel tortuosity, diameter, and transmural pressure,” Graefe's Arch. Clin. Exp. Ophthalmol., 224, 477–480, 1986.
- International Committee for the Classification of Retinopathy of Prematurity. “The International Classification of Retinopathy of Prematurity revisited,” Arch. Ophthalmol., 123 (7), 991-999, 2005.
- X. H. Zhang, R. L. Ning and D. Yang, “Cone beam breast CT noise reduction using 3D adaptive Gaussian filtering,” J. X-Ray Sci. Technol., 17 (4), 319-333, 2009.
- R. Roy, M. Pal and T. Gulati, “Zooming digital images using interpolation techniques,” Int. J. Innov. Technol. Manag., 2 (4), 34-45, 2013.
- A. K. Singh, P. Kang. (2022, Nov 27). Log Transformation. [Online] Avaliable: https://theailearner.com/2019/05/25/laplacian-of-gaussian-log/
- G. T. Reid, “Automatic fringe pattern analysis: A review,” Opt Lasers Eng, 7 (1), 37-68, 1986.
- R. Ritika and S. Kaur, “Contrast enhancement techniques for images–a visual analysis,” Int.J. Comput. App., 64 (17), 20-25 2013.
- C. R. Dyer, Multiscale Image Understanding. New York: Academic Press, 1987, pp. 171-213.
- N. Otsu, “A threshold selection method from gray-level histograms,” in IEEE Transactions on Systems, Man, and Cybernetics, 9 (1), 62-66, 1979.
- M. Sezgin, “İmge eşikleme yöntemlerinin başarım değerlendirmesi ve tahribatsız muayenede kullanımı,” Doktora Tezi, Uçak Mühendisliği, İTÜ, İstanbul, Türkiye, 2002.
- W. Niblack, An introduction to digital image processing. Englewood Cliffs:Prentice hall. 1986, pp. 115-116. [19] J. Kittler and J. Illingworth, “Minimum error thresholding,” Pattern Recognit, 19 (1), 41-47, 1986.
- J.N. Kapur, P.K. Sahoo, and A. K. C. Wong, “A new method for gray-level picture thresholding using the entropy of the histogram,” Comput. Gr. Image Process, 29 (3), 273-285, 1985.
- P. Nikhil, R. Pal and K. P. Sankar, “Entropic thresholding,” IEEE Trans. Signal Process, 16 (2), 97-108, 1989.
- C. H. Li and C. K. Lee, “Minimum cross entropy thresholding,” Pattern Recognit, 26 (4), 617-625, 1993.
- A. Elen, “Görüntü ikileştirme için global eşikleme yöntemleri üzerine bir inceleme,”Mühendislik Bilimleri ve Araştırmaları Dergisi, 2, 38-49, 2020.
- G. W. Zack, W. E., Rogers and S. A. Latt, “Automatic measurement of sister chromatid exchange frequency,” J. Histochem. Cytochem, 25(7), 741–53, 1977.
- Y. Üncü, S. Gençay ve M. Canpolat, “Difüz optik tomografi sisteminde görüntü işleme uygulamalarinin test edilmesi,” Süleyman Demirel Üniversitesi Fen Edebiyat Fakültesi Fen Dergisi, 16(1), 1-16, 2021.
- Z. Wang, A. C. Bovik, H. R. Şeyh and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE T Image Process, 13 (4), 600–612,2004.
- Y. Gao, A. Rehman and Z. Wang. “CW-SSIM based image classification,” In IEEE International Conference on Image Processing ICIP, Brussels, Belgium, 2011, pp. 1249–1252.
- Z. Li, C. Liu, G.Liu, Y. Cheng, X. Yang and C. Zhao, “A novel statistical image thresholding method”, AEU - International Journal of Electronics and Communications, 64 (12), 1137-1147, 2010.
- H. Jeong, T. Kim, H. Hwang, H. Choi, H. Park and H. K. Choi, “Comparison of thresholding methods for breast tumor cell segmentation,” Proceedings of 7th International Workshop on Enterprise networking and Computing in Healthcare Industry, 2005, pp. 392-395.
- J. Anitha, S. I. A. Pandian and S. A. Agnes, “An efficient multilevel color image thresholding based on modified whale optimization algorithm,” Expert Systems with Applications, 178, 115003, 2021.
- N. M. Nimbarte and M. M. Mushrif, “Multi-level thresholding algorithm for color image segmentation,” 2010 Second International Conference on Computer Engineering and Applications, Bali, Indonesia, 2010, pp. 231-233.
Prematüre Retina Kan Damarlarının Tespitinde Farklı Görüntü İşleme Yöntemlerinin Performanslarının Karşılaştırılması
Öz
Prematüre retinopatisi (ROP) hastalığının teşhisinde, retinadaki kan damarlarının özellikleri oldukça önemlidir. Erken doğan bebeklerde retina kan damarları büyümesini tamamlayamaz. Doğum sonrası, bazı tetikleyici durumlar düzenli damarların oluşumunun durmasına ve anormal kan damarlarının anormal doku oluşturarak genişlemeye başlamasına neden olur. Bu durumunun derecesinin artması ile retina hasarları oluşabilir. Özellikle prematüre doğan bebeklerin, prematüre retinopatisi hastalığı kapsamında olduğu için, damar ağlarında gelişmeleri takip ederek hastalığın seyrini takip etmek önemlidir. Bu çalışmada amacımız, prematüre bebeklerin retina görüntüleri üzerine görüntü işleme yöntemleri uygulayarak ROP görüntülerindeki damar yollarının tespiti ve segmentasyonu için yöntemler geliştirmektir. Uygulanan bu yöntemler, ROP görüntülerine uygulanarak sonuçlar sayısal olarak karşılaştırılmıştır. Sonuç olarak, en uygun görüntü işleme yönteminin, Tepe sinyalinin gürültüye oranı (PSNR) değerlerine göre; eşikleme yönteminde OTSU, filtreleme algoritmasında ise Gaussian filtresinin olduğu saptanmıştır.
Anahtar Kelimeler
Kaynakça
- E. Koç, A. Baş, Ş. Özdek ve F. Ovalı. (2021) Türkiye Prematüre Retinopatisi Rehberi 2021 [Online]. Avaiable: https://www.todnet.org/tod-rehber/rop-tedavi-rehberi-2021.pdf
- J. J. Kanski, Clinical Ophthalmology: A Systemic Approach, 6th ed. London, UK: Elsevier Health Sciences, 2007, pp. 952-955.
- F. Zana and J. C. Klein, “Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation,” IEEE Trans. Image Process, 10 (7), 1010–1019, 2001.
- B. Toptaş ve D. Hanbay, “Retina kan damarlarını çıkarmak için eşikleme temelli morfolojik bir yöntem,” NÖHÜ Müh. Bilim. Derg, 11 (1), 010-016, 2022.
- M. Niemeijer, J. Staal, B. Van Ginneken, M. Loog and M. D. Abramoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” In Medical imaging 2004: image processing SPIE, 5370, 648-656, 2004.
- R. GeethaRamani, L. Balasubramanian, “Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis,” Biocybernetics and Biomedical Engineering, 36 (1), 102-118, 2016.
- R. Y. Foos, “Chronic retinopathy of prematurity,” Ophthalmology, 92 (4), 563-74, 1985.
- J. A. Kylstra and T. Wierzbicki, M. L. Wolbarsht,”The relationship between retinal vessel tortuosity, diameter, and transmural pressure,” Graefe's Arch. Clin. Exp. Ophthalmol., 224, 477–480, 1986.
- International Committee for the Classification of Retinopathy of Prematurity. “The International Classification of Retinopathy of Prematurity revisited,” Arch. Ophthalmol., 123 (7), 991-999, 2005.
- X. H. Zhang, R. L. Ning and D. Yang, “Cone beam breast CT noise reduction using 3D adaptive Gaussian filtering,” J. X-Ray Sci. Technol., 17 (4), 319-333, 2009.
- R. Roy, M. Pal and T. Gulati, “Zooming digital images using interpolation techniques,” Int. J. Innov. Technol. Manag., 2 (4), 34-45, 2013.
- A. K. Singh, P. Kang. (2022, Nov 27). Log Transformation. [Online] Avaliable: https://theailearner.com/2019/05/25/laplacian-of-gaussian-log/
- G. T. Reid, “Automatic fringe pattern analysis: A review,” Opt Lasers Eng, 7 (1), 37-68, 1986.
- R. Ritika and S. Kaur, “Contrast enhancement techniques for images–a visual analysis,” Int.J. Comput. App., 64 (17), 20-25 2013.
- C. R. Dyer, Multiscale Image Understanding. New York: Academic Press, 1987, pp. 171-213.
- N. Otsu, “A threshold selection method from gray-level histograms,” in IEEE Transactions on Systems, Man, and Cybernetics, 9 (1), 62-66, 1979.
- M. Sezgin, “İmge eşikleme yöntemlerinin başarım değerlendirmesi ve tahribatsız muayenede kullanımı,” Doktora Tezi, Uçak Mühendisliği, İTÜ, İstanbul, Türkiye, 2002.
- W. Niblack, An introduction to digital image processing. Englewood Cliffs:Prentice hall. 1986, pp. 115-116. [19] J. Kittler and J. Illingworth, “Minimum error thresholding,” Pattern Recognit, 19 (1), 41-47, 1986.
- J.N. Kapur, P.K. Sahoo, and A. K. C. Wong, “A new method for gray-level picture thresholding using the entropy of the histogram,” Comput. Gr. Image Process, 29 (3), 273-285, 1985.
- P. Nikhil, R. Pal and K. P. Sankar, “Entropic thresholding,” IEEE Trans. Signal Process, 16 (2), 97-108, 1989.
- C. H. Li and C. K. Lee, “Minimum cross entropy thresholding,” Pattern Recognit, 26 (4), 617-625, 1993.
- A. Elen, “Görüntü ikileştirme için global eşikleme yöntemleri üzerine bir inceleme,”Mühendislik Bilimleri ve Araştırmaları Dergisi, 2, 38-49, 2020.
- G. W. Zack, W. E., Rogers and S. A. Latt, “Automatic measurement of sister chromatid exchange frequency,” J. Histochem. Cytochem, 25(7), 741–53, 1977.
- Y. Üncü, S. Gençay ve M. Canpolat, “Difüz optik tomografi sisteminde görüntü işleme uygulamalarinin test edilmesi,” Süleyman Demirel Üniversitesi Fen Edebiyat Fakültesi Fen Dergisi, 16(1), 1-16, 2021.
- Z. Wang, A. C. Bovik, H. R. Şeyh and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE T Image Process, 13 (4), 600–612,2004.
- Y. Gao, A. Rehman and Z. Wang. “CW-SSIM based image classification,” In IEEE International Conference on Image Processing ICIP, Brussels, Belgium, 2011, pp. 1249–1252.
- Z. Li, C. Liu, G.Liu, Y. Cheng, X. Yang and C. Zhao, “A novel statistical image thresholding method”, AEU - International Journal of Electronics and Communications, 64 (12), 1137-1147, 2010.
- H. Jeong, T. Kim, H. Hwang, H. Choi, H. Park and H. K. Choi, “Comparison of thresholding methods for breast tumor cell segmentation,” Proceedings of 7th International Workshop on Enterprise networking and Computing in Healthcare Industry, 2005, pp. 392-395.
- J. Anitha, S. I. A. Pandian and S. A. Agnes, “An efficient multilevel color image thresholding based on modified whale optimization algorithm,” Expert Systems with Applications, 178, 115003, 2021.
- N. M. Nimbarte and M. M. Mushrif, “Multi-level thresholding algorithm for color image segmentation,” 2010 Second International Conference on Computer Engineering and Applications, Bali, Indonesia, 2010, pp. 231-233.
Toplam 30 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Metroloji,Uygulamalı ve Endüstriyel Fizik |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 22 Haziran 2023 |
| Yayımlandığı Sayı | Yıl 2023 Cilt: 18 Sayı: 2 |
