Abstract
Diabetes retinopathy (DR) is one of the leading cause of blindness among people suffering from diabetes. It is a lesion based disease which starts off as small red spots on the retina. These small red lesions are known as microaneurysms (MA). These microaneurysms gradually increase in size as the DR progresses, which eventually leads to blindness. Thus, DR can be prevented at a very early stage by eliminating the retinal microaneurysms. However, elimination of MA is a two step process. The first step requires detecting the presence of MA on the retina. The second step involves pinpointing the location of MA on the retina. Even though, these two steps are interdependent, there is no model available that can perform both steps simultaneously. Most of the models perform the first step successfully, while the second step is performed by opthamologists manually. Hence we have proposed an object detection model that integrates the two steps by detecting (first step) and pinpointing (second step) the MA on the retina simultaneously. This would help the opthamologists in directly finding the exact location of MA on the retina, thereby simplifying the process and eliminating any manual intervention.
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References
Wilkinson CP, Ferris FL, Klein RE et al. Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology. 2003;110:1677–82.
Acharya UR et al. Computer-based detection of diabetes retinopathy stages using digital fundus images. Proc Inst Mech Eng Part H J Eng Med. 2009;223(5):545–53.
Silva PS et al. Hemorrhage and/or microaneurysm severity and count in ultrawide field images and early treatment diabetic retinopathy study photography. Ophthalmology. 2017;124(7):970–6.
Acharya UR, Ng EYK, Suri JS. Image modelling of human eye. Norwood: Artech House; 2008.
Fleming AD et al. Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Trans Med Imaging. 2006;25(9):1223–32.
Wiley HE, Ferris FL III. Nonproliferative diabetic retinopathy and diabetic macular edema. In: Ryan SJSS, Hinton DR, editors. Retina. London: Elsevier Saunders; 2013. p. 940–968.
Ezra E et al. Non-dimensional analysis of retinal microaneurysms: critical threshold for treatment. Integr Biol. 2013;5(3):474–80.
Antal B, Hajdu A. An ensemble-based system for microaneurysm detection and diabetic retinopathy grading. IEEE Trans Biomed Eng. 2012;59(6):1720–6.
Wu B et al. Automatic detection of microaneurysms in retinal fundus images. Comput Med Imaging Gr. 2017;55:106–12.
Habib M et al. Incorporating spatial information for microaneurysm detection in retinal images. Adv Sci Technol Eng Syst J. 2017;2(3):642–9.
Javidi M, Pourreza H-R, Harati A. Vessel segmentation and microaneurysm detection using discriminative dictionary learning and sparse representation. Comput Methods Programs Biomed. 2017;139:93–108.
Shah SAA et al. Automated microaneurysm detection in diabetic retinopathy using curvelet transform. J Biomed Opt. 2016;21(10):101404.
Zhou W et al. Automatic microaneurysm detection using the sparse principal component analysis-based unsupervised classification method. IEEE Access. 2017;5:2563–72.
Zhou W, Wu C, Chen D, Wang Z, Yi Y, Du W. Automatic microaneurysms detection based on multifeature fusion dictionary learning. Comput Math Methods Med. 2017. https://doi.org/10.1155/2017/2483137.
Chudzik P et al. Microaneurysm detection using fully convolutional neural networks. Comput Methods Programs Biomed. 2018;158:185–92.
Haloi M. Improved microaneurysm detection using deep neural networks. 2015. arXiv preprint arXiv:1505.04424.
Lam C et al. Retinal lesion detection with deep learning using image patches. Investig Ophthalmol Vis Sci. 2018;59(1):590–6.
(2015, February 17). [Online]. https://www.kaggle.com/c/diabetic-retinopathy-detection.
Kumar M, Nath MK. Detection of microaneurysms and exudates from color fundus images by using SBGFRLS algorithm. In: Proceedings of the international conference on informatics and analytics. ACM; 2016.
Sehirli E, Turan MK, Dietzel A. Automatic detection of microaneurysms in rgb retinal fundus images. Studies. 2015;1(8):1–7.
Ishibazawa A et al. Optical coherence tomography angiography in diabetic retinopathy: a prospective pilot study. Am J Ophthalmol. 2015;160(1):35–44.
Redmon J et al. You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
Redmon J, Farhadi A. YOLO9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition 2017.
Ghosh R, Ghosh K, Maitra S. Automatic detection and classification of diabetic retinopathy stages using CNN. In: 2017 4th international conference on signal processing and integrated networks (SPIN). IEEE; 2017.
Streeter L, Cree MJ. Microaneurysm detection in colour fundus images. In: Image and vision computing. New Zealand, Palmerston North, New Zealand, 2003, p. 280–284.
Cortés-Ancos E, Gegúndez-Arias ME, Marin D. Microaneurysm candidate extraction methodology in retinal images for the integration into classification-based detection systems. In: International Conference on Bioinformatics and Biomedical Engineering. Cham: Springer; 2017.
Cervera MMÁ et al. Development of a detection system microaneurysms in color fundus images. In: 2016 13th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE). IEEE; 2016.
Hatanaka Y et al. Automated microaneurysm detection in retinal fundus images based on the combination of three detectors. J Med Imaging Health Inform. 2018;8(5):1096–102.
Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern. 1979;9(1):62–6.
[Online] https://pjreddie.com/darknet/yolov2/.
Xiang Yu, Mottaghi R, Savarese S. Beyond pascal: a benchmark for 3d object detection in the wild. In: IEEE Winter Conference on Applications of Computer Vision. IEEE; 2014.
Farfade SS, Saberian MJ, Li L-J. Multi-view face detection using deep convolutional neural networks. In: Proceedings of the 5th ACM on international conference on multimedia retrieval. ACM; 2015.
Schöller FET et al. Assessing deep-learning methods for object detection at sea from LWIR images. In: IFAC Workshop Series. No. Special issue. Elsevier Ltd. Books Division; 2019.
Aishwarya R, Vasundhara T, Ramachandran KI. A hybrid classifier for the detection of microaneurysms in diabetic retinal images. In: The 16th international conference on biomedical engineering. Singapore: Springer; 2017.
Manjaramkar A, Kokare M. A rule based expert system for microaneurysm detection in digital fundus images. In: 2016 International conference on computational techniques in information and communication technologies (ICCTICT). IEEE; 2016.
Manohar P, Singh V. Morphological approach for Retinal Microaneurysm detection. In: 2018 Second international conference on advances in electronics, computers and communications (ICAECC). IEEE; 2018.
Sopharak A, Uyyanonvara B, Barman S. Simple hybrid method for fine microaneurysm detection from non-dilated diabetic retinopathy retinal images. Comput Med Imaging Gr. 2013;37(5–6):394–402.
Badgujar RD., Deore PJ. Region growing based segmentation using forstner corner detection theory for accurate microaneurysms detection in retinal fundus images. In: 2018 Fourth international conference on computing communication control and automation (ICCUBEA). IEEE; 2018.
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Akut, R.R. FILM: finding the location of microaneurysms on the retina. Biomed. Eng. Lett. 9, 497–506 (2019). https://doi.org/10.1007/s13534-019-00136-6
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DOI: https://doi.org/10.1007/s13534-019-00136-6