Automated system for detection and classification of cystoid macular oedema using OCT images

References

• Abhishek AM, Berendschot TTJM, Rao SV, Dabir S 2014. Segmentation and analysis of retinal layers (ILM & RPE) in optical coherence tomography images with edema. Proceedings of the IEEE Conference on Biomedical Engineering and Sciences (IECBES); December 08-10; Kuala Lumpur: IEEE. p. 204–209.
   Google Scholar
• Amini Z, Rabbani H. 2016. Statistical modeling of retinal optical coherence tomography. IEEE Trans Med Imaging. 35(6):1544–1554. doi: 10.1109/TMI.2016.2519439.
   PubMed Web of Science ®Google Scholar
• Anantrasirichai N, Nicholson L, Morgan JE, Erchova I, Achim A 2013. Adaptive-weighted bilateral filtering for optical coherence tomography. Proceedings of the IEEE International Conference on Image Processing; September 15-18; Melbourne, VIC, Australia: IEEE. p. 1110–1114.
   Google Scholar
• Cheng J, Tao D, Quan Y, Wong DWK, Cheung GCM, Akiba M, Liu J. 2016. Speckle reduction in 3D optical coherence tomography of retina by A-Scan reconstruction. IEEE Trans Med Imaging. 35(10):2270–2279. doi: 10.1109/TMI.2016.2556080.
   PubMed Web of Science ®Google Scholar
• Chen Z, Li D, Shen H, Mo H, Zeng Z, Wei H. 2020. Automated segmentation of fluid regions in optical coherence tomography B-scan images of age-related macular degeneration. Opt Laser Technol. 122:105830. doi: 10.1016/j.optlastec.2019.105830.
   Web of Science ®Google Scholar
• Chiu SJ, Li XT, Nicholas P, Toth CA, Izatt JA, Farsiu S. 2010. Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation. Opt Express. 18(18):19413–19428. doi: 10.1364/OE.18.019413.
   PubMed Web of Science ®Google Scholar
• Danya P, Rao SN. 2017. Retinal macular edema detection using optical coherence tomography images. IOSR J VLSI Signal Process (IOSR-JVSP). 7(2):47–52. doi: 10.9790/4200-0702014752.
   Google Scholar
• Deshpande A, Estrela VV, Razmjooy N, Editors. 2021. Computational intelligence methods for super-resolution in image processing applications. Cham, Switzerland: Springer International Publishing.
   Google Scholar
• Duan J, Tench C, Gottlob I, Proudlock F, Bai L 2015. Optical coherence tomography image segmentation. Proceedings of IEEE International Conference on Image Processing (ICIP);September 27-30; Quebec City, QC, Canada: IEEE. p. 4278–4282.
   Google Scholar
• Gonzalez RC, Woods RE. 2008. Digital image processing: upper saddle. River, N.J: Prentice Hall.
   Google Scholar
• Hassan B, Ahmed R, Li B, Hassan O, Hassan T 2019. Automated retinal edema detection from fundus and optical coherence tomography scans. Proceedings of the 5th International Conference on Control, Automation and Robotics (ICCAR); April 19-22; Beijing, China: IEEE. p. 325–330.
   Google Scholar
• Hassan B, Raja G 2016, Fully automated assessment of macular edema using Optical Coherence Tomography (OCT) images. Proceedings of the International Conference on Intelligent Systems Engineering (ICISE); January 15-17; Islamabad, Pakistan: IEEE. p. 5–9.
   Google Scholar
• Helmy YM, Allah HRA. 2013. Optical coherence tomography classification of diabetic cystoid macular edema. Clin Ophthalmol. 7:1731–1737. doi: 10.2147/OPTH.S47987.
   PubMedGoogle Scholar
• Huang D, Swanson EA, Lin CP, Schuman JS, Stinson WG, Chang W, Hee MR, Flotte T, Gregory K, Puliafito CA, et al. 1991. Optical coherence tomography. Science. 254(5035):1178–1181. doi: 10.1126/science.1957169.
   PubMed Web of Science ®Google Scholar
• Hu J, Chen Y, Yi Z. 2019. Automated segmentation of macular edema in OCT using deep neural networks. Med Image Anal. 55:216–225. doi: 10.1016/j.media.2019.05.002.
   PubMed Web of Science ®Google Scholar
• Lang A, Carass A, Hauser M, Sotirchos ES, Calabresi PA, Ying HS, Prince JL. 2013. Retinal layer segmentation of macular OCT images using boundary classification. Biomed Opt Express. 4(7):1133–1152. doi: 10.1364/BOE.4.001133.
   PubMed Web of Science ®Google Scholar
• Padmasini N, Abbirame KS, Yacin SM, Umamaheswari R 2014. Speckle noise reduction in spectral domain optical coherence tomography retinal images using anisotropic diffusion filtering. Proceedings of the International Conference on Science Engineering and Management Research (ICSEMR); November 27-29; Chennai, India: IEEE. p. 1–5.
   Google Scholar
• Rajinikanth V, Razmjooy N, Ghadimi N. 2022. Design of a system for melanoma diagnosis using image processing and hybrid optimization techniques. In Chapter 5, frontiers of artificial intelligence in medical imaging. IOP Publishing; pp. 5-1 to 5–38. doi: 10.1088/978-0-7503-4012-0ch5.
   Google Scholar
• Ranjbarzadeh R, Bagherian Kasgari A, Jafarzadeh Ghoushchi S, Anari S, Naseri M, Bendechache M. 2021. Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images. Sci Rep. 11(1):10930. doi: 10.1038/s41598-021-90428-8.
   PubMed Web of Science ®Google Scholar
• Ranjbarzadeh R, Caputo A, Tirkolaee EB, Jafarzadeh Ghoushchi S, Bendechache M. 2023. Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools. Comput Biol Med. 152:106405. doi: 10.1016/j.compbiomed.2022.106405.
   PubMed Web of Science ®Google Scholar
• Ranjbarzadeh R, Dorosti S, Jafarzadeh Ghoushchi S, Caputo A, Tirkolaee EB, Ali SS, Arshadi Z, Bendechache M. 2023. Breast tumor localization and segmentation using machine learning techniques: Overview of datasets, findings, and methods. Comput Biol Med. 152:106443. doi: 10.1016/j.compbiomed.2022.106443.
   PubMed Web of Science ®Google Scholar
• Rotsos TG, Moschos MM. 2008. Cystoid macular edema. Clin Ophthalmol. 2(4):919–930. doi: 10.2147/OPTH.S4033.
   PubMedGoogle Scholar
• Roychowdhury S, Koozekanani DD, Radwan S, Parhi KK 2013. Automated localization of cysts in diabetic macular edema using optical coherence tomography images. Proceedings of 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); July 3-7; Osaka, Japan: IEEE. p. 1426–1429.
   Google Scholar
• Samagaio G, Estvez A, Moura J, Novo J, Fernandez MI, Ortega M. 2018. Automatic macular edema identification and characterization using OCT images. Comput Methods Programs Biomed. 163:47–63. doi: 10.1016/j.cmpb.2018.05.033.
   PubMed Web of Science ®Google Scholar
• Srinivasan PP, Kim LA, Mettu PS, Cousins SW, Comer GW, Izatt JA, Farsiu S. 2014. Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed Opt Express. 5(10):3568–3577. doi: 10.1364/BOE.5.003568.
   PubMed Web of Science ®Google Scholar
• Vidal PL, de Moura J, Diaz M, Novo J, Ortega M. 2020. Diabetic macular edema characterization and visualization using optical coherence tomography images. Appl Sci. 10(21):7718. doi: 10.3390/app10217718.
   Google Scholar
• Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. 2004. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 13(4):600–612. doi: 10.1109/TIP.2003.819861.
   PubMed Web of Science ®Google Scholar
• Wu J, Philip AM, Podkowinski D, Gerendas BS, Langs G, Simader C, Waldstein SM, Schmidt-Erfurth UM. 2016. Multivendor spectral-domain optical coherence tomography dataset, observer annotation performance evaluation, and standardized evaluation framework for intraretinal cystoid fluid segmentation. J Ophthalmol. 2016(3):1–8. doi: 10.1155/2016/3898750.
   Google Scholar
• Xu Y, Wang Y, Razmjooy N. 2022. Lung cancer diagnosis in CT images based on Alexnet optimized by modified Bowerbird optimization algorithm. Biomed Signal Process Control. 77:103791. doi: 10.1016/j.bspc.2022.103791.
   Web of Science ®Google Scholar