Diagnosis of Retinitis Pigmentosa from Retinal Images

Giritharan Ravichandran, Malaya Kumar Nath, Poonguzhali Elangovan


Retinitis pigmentosa is a genetic disorder that results in nyctalopia and its progression leads to complete loss of vision. The analysis and the study of retinal images are necessary, so as to help ophthalmologist in early detection of the retinitis pigmentosa. In this paper fundus images and Optical Coherence Tomography images are comprehensively analyzed, so as to obtain the various morphological features that characterize the retinitis pigmentosa. Pigment Deposits, important trait of RP is investigated. Degree of darkness and entropy are the features used for analysis of PD. The darkness and entropy of the PD is compared with the different regions of the fundus image which is used to detect the pigments in the retinal image. Also the performance of the proposed algorithm is evaluated by using various performance metrics. The performance metrics are calculated for all 120 images of RIPS dataset. The performance metrics such as sensitivity, sensibility, specificity, accuracy, F-score, equal error rate, conformity coefficient, Jaccard's coefficient, dice coefficient, universal quality index were calculated as 0.72, 0.96, 0.97, 0.62, 0.12, 0.09, 0.59, 0.45 and 0.62, respectively.

Full Text:



E. Nakano, M. Hata, A. J. Oishi, K. Miyamoto, A. Uji, M. Fujimoto, M. Miyata, and N. Yoshimura, “Quantitative comparison of disc rim color in optic nerve atrophy of compressive optic neuropathy and glaucomatous optic neuropathy,” Graefe’s Archive for Clinical and Experimental Ophthalmology, vol. 254, pp. 1609–1616, 2016.

D. C. Hood, M. A. Lazow, K. G. Locke, V. C. Greenstein, and D. G. Birch, “The transition zone between healthy and diseased retina in patients with retinitis pigmentosa,” Investigative Ophthalmology and Visual Science, vol. 52, no. 1, p. 101, 2011.

H. Das, A. Saha, and S. Deb, “An expert system to distinguish a defective eye from a normal eye,” in 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), pp. 155–158,

Feb 2014.

C. Hamel, “Retinitis pigmentosa,” Orphanet Journal of Rare Diseases, vol. 1, p. 40, Oct 2006.

Q. Yang, C. A. Reisman, K. Chan, R. Ramachandran, A. Raza, and D. C. Hood, “Automated segmentation of outer retinal layers in macular oct images of patients with retinitis pigmentosa,” Biomed. Opt. Express, vol. 2, pp. 2493–2503, Sep 2011.

D. T. Hartong, P. E. L. Berson, and P. T. P. Dryja, “Retinitis pigmentosa,” The Lancet, vol. 368, no. 9549, pp. 1795–1809, 2006.

Y. Xu, L. Guan, X. Xiao, J. Zhang, S. Li, H. Jiang, X. Jia, J. Yang, X. Guo, Y. Yin, J. Wang, and Q. Zhang, “Mutation analysis in 129 genes associated with other forms of retinal dystrophy in 157 families with retinitis pigmentosa based on exome sequencing,” in Molecular vision, 2015.

P. D. Pozzo, E. Cardaioli, E. Malfatti, G. N. Gallus, A. Malandrini, C. Gaudiano, G. Berti, F. Invernizzi, M. Zeviani, and A. Federico, “’a novel mutation in the mitochondrial trnapro gene associated with lateonset ataxia, retinitis pigmentosa, deafness, leukoencephalopathy and complex i deficiency,” European journal of human gentics, vol. 17, pp. 1092–1096, 2009.

K. Kajiwara, E. L. Berson, and T. P. Dryja, “Digenic retinitis pigmentosa due to mutations at the unlinked peripherin/rds and rom1 loci.,” Science, vol. 264 5165, pp. 1604–8, 1994.

L. Li, N. Khan, T. Hurd, A. K. Ghosh, C. Cheng, R. Molday, J. R. Heckenlively, A. Swaroop, and H. Khanna, “Ablation of the x-linked retinitis pigmentosa 2 (rp2) gene in mice results in opsin mislocalization and photoreceptor degeneration,” Investigative Ophthalmology and Visual Science, vol. 54, no. 7, p. 4503, 2013.

G. P. Theodossiadis and S. N. Kokolakis, “’macular pigment deposits in rhegmatogenous retinal detachment,” British Journal of Ophthalmology, vol. 63, pp. 498 – 506, Jul 1979.

M. Frucci, D. Riccio, G. S. di Baja, and L. Serino, “Severe: Segmenting vessels in retina images,” Pattern Recognition Letters, vol. 82, pp. 162 – 169, 2016. An insight on eye biometrics.

S. Goswami, S. Goswami, and S. De, “Automatic measurement and analysis of vessel width in retinal fundus image,” in Proceedings of the First International Conference on Intelligent Computing and Communication (J. K. Mandal, S. C. Satapathy, M. K. Sanyal, and V. Bhateja, eds.), (Singapore), pp. 451–458, Springer Singapore, 2017.

D. Popescu and L. Ichim, “Intelligent image processing system for detection and segmentation of regions of interest in retinal images,” Symmetry, vol. 10, no. 3, 2018.

B. Zhang, L. Zhang, L. Zhang, and F. Karray, “Retinal vessel extraction by matched filter with first-order derivative of gaussian,” Computers in Biology and Medicine, vol. 40, no. 4, pp. 438 – 445, 2010.

Q. Yuanzhen, W. Y. Xing, X. Liang, Z. Liwei, Z. Junting, Z. Jianguo,

W. Lina, Y. Liu, Y. Anchao, W. Jian, and J. J. B., “Glaucoma-like optic neuropathy in patients with intracranial tumours,” Acta Ophthalmologica, vol. 89, no. 5, pp. e428–e433, 2011.

N. Brancati, M. Frucci, D. Gragnaniello, D. Riccio, V. D. Iorio, L. D. Perna, and F. Simonelli, “Learning-based approach to segment pigment signs in fundus images for retinitis pigmentosa analysis,” Neurocomputing, vol. 308, pp. 159 – 171, 2018.

N. M. Kumar and D. Samarendra, “Differential entropy in wavelet subband for assessment of glaucoma,” International Journal of Imaging Systems and Technology, vol. 22, no. 3, pp. 161–165.

K. Udayakumar, P. P. Bharati, S. Verma, and M. K. Nath, “Automatic estimation of vision degradation from color fundus image,” International Journal of Image, Graphics and Signal Processing, vol. 7, no. 11, pp. 26–34, 2015.


  • There are currently no refbacks.

International Journal of Electronics and Telecommunications
is a periodical of Electronics and Telecommunications Committee
of Polish Academy of Sciences

eISSN: 2300-1933