Retinal Image Processing and Classification Using Convolutional Neural Networks
This study aims to develop a system to distinguish retinal disease from fundus images. Precise and programmed analysis of retinal images has been considered as an effective way for the determination of retinal diseases such as diabetic retinopathy, hypertension, arteriosclerosis, etc. In this work, we extracted different retinal features such as blood vessels, optic disc and lesions and then applied convolutional neural network based models for the detection of multiple retinal diseases with fundus photographs involved in structured analysis of the retina (STARE) database. Augmentation techniques like translations and rotations are done for expanding the number of images. The blood vessel extraction is done with the help of morphological operations like dilation and erosion and enhancement operations like CLAHE and AHE. The optic disc is localized by the methods such as opening, closing, Canny’s edge detection and finally thresholding the image after filling the holes. The bright lesions (exudates) inside the retina are detected by the filtering operations and contrast enhancement after the removal of the optic disc. In this study, we experimented with different retinal features as input to convolutional neural networks for effective classification of retinal images.
KeywordsAHE Canny’s edge detection CLAHE Convolutional neural networks Dilation Erosion Lesions Optic nerve Segmentation STARE
This work was fully funded by Calpine Labs, UVJ Technologies, Kochi, India. We are also immensely grateful to Mr. Bijeesh Devassy, Project Manager, UVJ Technologies and Dr. Asharaf S, Associate Professor, IIITM-K for sharing their pearls of wisdom with us during the course of this research.
- 6.Sánchez CI, Hornero R, Lopez MI, Poza J (2004) Retinal image analysis to detect and quantify lesions associated with diabetic retinopathy. In: 26thAnnual International Conference of the IEEE engineering in medicine and biology society, IEMBS’04, vol 1. IEEE, pp 1624–1627Google Scholar
- 7.Mishra M, Nath MK, Dandapat S (2011) Glaucoma detection from color fundus images. Int J Comput Commun Technol (IJCCT) 2(6):7–10Google Scholar
- 9.Verma K, Deep P, Ramakrishnan A (2011) Detection and classification of diabetic retinopathy using retinal images. In: 2011 Annual IEEE India conference (INDICON), pp 1–6. IEEEGoogle Scholar
- 10.Priyadharsini BH Devi MR (2014) Analysis of retinal blood vessels using image processing techniques. In: 2014 international conference on intelligent computing applications (ICICA), pp. 244–248. IEEEGoogle Scholar
- 11.Tjandrasa H, Wijayanti A, Suciati N (2012) Optic nerve head segmentation using hough transform and active contours. Indones J Electr Eng Comput Sci 10(3):531–536Google Scholar
- 13.Radha R, Lakshman B (2013) Retinal image analysis using morphological process and clustering technique. Signal Image Process 4(6):55Google Scholar
- 14.Wang H, Hsu W, Goh KG, Lee ML (2000) An effective approach to detect lesions in color retinal images. In: 2000 Proceedings of IEEE conference on computer vision and pattern recognition, vol 2. pp. 181–186, IEEEGoogle Scholar
- 16.Melinščak M, Prentašić P, Lončarić S (2015) Retinal vessel segmentation using deep neural networks. In: VISAPPGoogle Scholar
- 17.Chandore V, Asati S (2017) Automatic detection of diabetic retinopathy using deep convolutional neural networkGoogle Scholar
- 18.Tan JH, Acharya UR, Bhandary SV, Chua KC, Sivaprasad S (2017) Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. J Comput Sci 20:70–79Google Scholar
- 19.Dasgupta A, Singh S (2017) A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation. In: 14th International symposium on biomedical imaging, pp. 248–251. IEEEGoogle Scholar
- 20.STARE Dataset, http://cecas.clemson.edu/~ahoover/stare/