Stroke Prognosis through Retinal Image Analysis

  • Jeena R S College of Engineering Trivandrum 2RajivGandhi Institute of development Studies, Trivandrum
  • Sukesh Kumar A RajivGandhi Institute of development Studies, Trivandrum, India
Keywords: Stroke, prognosis, retinal image, retinex, retinal ischemia

Abstract

Many eye diseases as well as systemic diseases usually used to manifest in the retina. The innovations in the field of retinal imaging have paved the way to the development of tools for assisting physicians in stroke prognosis. Stroke is one of the leading causes of adult disability in most of the developing countries. Diagnosis of stroke during the initial stage is crucial for timely prevention and cure. Retinal imaging provides a non invasive technique of predicting the possibility of stroke. This research work focuses on the prediction of retinal ischemia from retinal fundus images and thereby predicting the occurance of stroke. Preprocessing of retinal images is done by retinex processing and morphological operations are done to remove noisy background. Branching points are detected and various features like major axis length, mean diameter, orientation, eccentricity, fractal dimension and tortuosity for the branching blood vessels are computed. This has been compared for various diseases like diabetic retinopathy, hypertensive retinopathy and retinal ischemia against a set of healthy retinal images. Classification has been implemented by Artificial Neural Networks which gives an accuracy of 89 % and the results proved to be promising.

References

(1) Barry L. Zaret, M.D., Marvin Moser, M.D., Lawrence S. Cohen, Chapter 18 Stroke - Lawrence M. Brass, M.D. (pgs 215-234)

(2) Baker ML,Wang JJ,Liew G et al,Differential associations of cortical and subcortical cerebral atropy with retinal vascular signs in patients with acute stroke,Stroke41,2143-50

(3) E. Land (1986).An alternative technique for the computation of the designator in the retinex theory of color vision, in Proc. Nat. Acad. Sci., vol. 83, pp.3078–3080.

(4) D. J. Jobson, Z. Rahman, and G. A. Woodell (1997). A Multiscale retinex for bridging the gap between color images and the human observation of scenes, IEEE Transaction Image Processing, Vol. 6, No. 7, pp. 965–976, July.

(5) Youhei Terai, TomioGoto, Satoshi Hirano, and Masaru Sakurai (2009). Color Image Contrast Enhancement by Retinex Model, Proceedings of IEEE 13th International Symposium on Consumer Electronics, pp. 392-393, May.

(6) Manjiri B. Patwari, Ramesh R. Manza, Yogesh M. Rajput, Deepali D. Rathod, Manoj Saswade, Neha Deshpande,"Classification and Calculation of Retinal Blood vessels Parameters", IEEE's INTERNATIONAL CONFERENCES FOR

CONVERGENCE OF TECHNOLOGY, Pune,India.

(7) Patwari Manjiri, Manza Ramesh, Rajput Yogesh, Saswade Manoj, Deshpande Neha,“Automated Localization of Optic Disk, Detection of Microaneurysms and Extraction of Blood Vessels to Bypass Angiography”, Springer, Advances in Intelligent Systems and Computing. ISBN: 978-3-319-11933-5, DOI: 10.1007/978-3-319-11933-5_65. 2014

(8) Yali Feng, Jing Huang, Zhuoli Feng and Minyong Liu (2011). The Research and Implementation of Light Compensation Algorithm in Color Facial Image, Electrical and Control Engineering Conference,pp.2758-276.

(9) Mrs. Anjali Chandra, BibhudendraAcharya &Mohammad Imroze Khan (2011).Retinex image processing: Improving the visual realism of color images, International Journal of Information Technology and Knowledge Management, Volume 4, No. 2, pp. 371-377

(10) Priyanka J K,Dr B G Sudarshanan,Dr S C Prasanna Kumar,Dr N Pradhan,”Development of algorithm for high resolution retinex for image enhancement”,International journal of Innovative Research development,December 2012.

(11) HT Nguyen, M Butler, A Roychoudhry, AG Shannon, J Flack, P Mitchell, ―Classification of Diabetic retinopathy using neural networks‖, 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Amsterdam 1996.

(12) María García, Roberto Hornero, Clara I. Sanchez, María I. Lopez and Ana Díez, ―Feature Extraction and Selection for the Automatic Detection of Hard Exudates in Retinal Images‖, Proceedings of the 29th Annual International Conference of the IEEE EMBS Cite Internationale, Lyon, France, August 23-26, 2007.

(13) Alireza Osareh , Majid Mirmehdi, Barry Thomas, and Richard Markham, ―Classification and Localisation of Diabetic-Related Eye Disease‖, Springer-Verlag Berlin Heidelberg ,pp. 502– 516, 2002

(14) Jian Wu, Guangming Zhang, Yanyan Cao, and Zhiming Cui, ‖ Research on Cerebral Aneurysm Image Recognition Method Using Bayesian Classification ―, Proceedings of the 2009 International Symposium on Information Processing (ISIP‘09), Huangshan, P. R. China, August 21-23, pp. 058-062, 2009.

(15) Yosawin Kangwanariyakul, Chanin Nantasenamat, Tanawut Tantimongcolwat, Thanaokorn Naenna, ‖ Data Mining Of Magnetocardiograms For Prediction of Ischemic Heart Disease‖ EXCLI Journal, 2010.

(16) Lili Xu, Shuqian Luo,‖ Support Vector Machine Based Method For Identifying Hard Exudates In Retinal Images‖, IEEE, 2009.

(17) Priya.R , Aruna.P, ‖ Automated Classification System For Early Detection Of Diabetic Retinopathy In Fundus Images‖, International Journal Of Applied Engineering Research, Dindigul, Volume 1, No 3,2010.

(18) J.J. Staal, M.D. Abramoff, M. Niemeijer, M.A. Viergever, B. van Ginneken, "Ridge based vessel segmentation in color images of the retina", IEEE Transactions on Medical Imaging, 2004, vol. 23, pp. 501-509.

(19) Behzad Aliahmad, D K Kumar, Hao Hao, Premith Unnikrishnan, M.Z Che Azemin, R Kawasaki, P Mitchell,Zone Specific fractal dimension of Retinal images as Predictor of Stroke Incidence, Hindawi Publication, Volume 2014

(20) Jeena R S,Dr SukeshKumar A,’Artificial Neural Networks in Stroke Prediction’, International Conference on Innovative Systems, December 2016,Bangalore

(21) Grisan E, Foracchia M, Ruggeri A (2008) A novel method for the automatic grading of retinal vessel tortuosity. IEEE Trans Med Imag 27, 310–9

(22) Bankhead P, Scholfield CN, McGeown JG, Curtis TM (2012) Fast Retinal Vessel Detection and Measurement Using Wavelets and Edge Location Refinement. PLoS ONE 7(3): e32435. doi:10.1371/journal.pone.0032435

(23) Yu, Chien-Cheng, and Bin-Da Liu. "A backpropagation algorithm with adaptive learning rate and momentum coefficient." Neural Networks, 2002. IJCNN'02. Proceedings of the 2002 International Joint Conference on. Vol. 2. IEEE, 2002.

(24) S. G. Vázquez, N. Barreira, M. G. Penedo, M. Ortega, A. Pose-Reino, "Improvements in Retinal Vessel Clustering Techniques:Towards the Automatic Computation of the Arterio Venous Ratio", Computing, Archives for Scientific Computing, 90 (3), 197-217, 2010.

Published
2017-05-10