Detection of micro aneurysms automatically for retinopathy screening

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Diabetic retinopathy is one among the severe diseases of eye leading to irreversible damage when left undiagnosed. Hence, robust automated system for diagnostic medical imaging is becoming a vital necessity in the healthcare industry. Microaneurysms are the first confirmed visual signs of onset of diabetic retinopathy. This work exploits the benefits of tristate median filtering and power law transform for improved candidate extraction work. It includes processing the image and normalization of intensities as microaneurysms are seen as low contrast and tiny.Tristate median filtering removes unwanted noise that would have added up while acquiring images. Power law transformation is applied to increase brightness. And makes the microaneurysms clearer in the image. The second approach involves extraction of retinal vessels from the image as miniature vessels can be falsely detected as candidate lesions. Weapply simple and effective morphological operations followed by detection of lesions by extended minima transform. Candidate features are extracted and then classified by K nearest Neighbour classifier.The performance of the proposed work is analysed giving accuracy specificity, and sensitivity values 91.5%, 82 %, 93% respectively.

     

     


  • Keywords


    Diabetes; Micro aneurysms; Retinal Vessels; Morphological Operation; Eye Fundus Image.

  • References


      [1] YingfengZheng, Mingguang He and Nathancongdon, ‘The worldwide epidemic of diabetic retinopathy’. Indian Journal Of Opthalmology,vol 6, 2012,pg 428-431.

      [2] Pedro RA, Ramon SA, Marc BB, Juan FB, Isabel MM, ‘Prevalence And Relationship Between Diabetic Retinopathy And Neuropathy ;And Its Risk Factors In The North- East Of Spain, A Population Based Study.’ Ophthalmic Epidemiol. 17(4) 2010, Pg 251-265.

      [3] P.Massin,A.Erginayand A. Gaudric, ‘ Retinopathie Diabetique’ ELSEVIER,SAS,2000.

      [4] Mohandass G, Hari Krishnan G, Hemalatha R J , An Approach To Automated Retinal Layer Segmentation In SDOCT Images, International Journal of Engineering and Technology- UAE, vol. 2.25, 2018.

      [5] B.Zhang, X.Wu,J.You,Q.Li , F.Karray, ‘Detection Of Microaneurysm Using Multiscale Correlation Coefficients.’ Patten Recognition, Vol43, 2010, Pp 2237-248.

      [6] R.Roy,S.Aruchamy,andP.Bhattacharjee,`` Detection of retinal microaneurysms using fractal analysis and feature extraction technique, `in proceedings of the 2nd international conference on communication and signal processing (ICCSP’13),pp.469-471,Melmaruvathur,India April 2013.

      [7] A.D. Fleming, S. Philip, K.A. Goatman, J.A. Olson, P.F. Sharp, Automated microaneurysm detection using local contrast normalization and local vessel detection, IEEE Transactions on Medical Imaging 25 (9) (2006) 1223–1232.

      [8] N.S Datta,H.S.Dutta,M.De,nad S.Mondal,``An effective approach:image quality enhancement for microaneurysms detection of non-dilated retinal FUNDUS IMAGE,”Procedia technology,vol.10,pp.731-737,2013.

      [9] The dynamic multiparameter template (DMPT) matching is applied for the detection of MAs that is more accurate as compared to conventional schemes. S.Ding and W.Ma,”An accurate approach for microaneurysm detection in 1851, IEEE,stocklim, Sweden, august 2014.

      [10] G. Quellec, M. Lamard, P.M. Josselin, G. Cazuguel, B. Cochener, C. Roux, Optimal wavelet transform for the detection of microaneurysms in retina photographs, IEEE Transactions on Medical Imaging 27 (9) (2008) 1230–1241.

      [11] I. Lazar, A. Hajdu, Microaneurysm detection in retinal images using a rotating cross-section based model. , 7906(1): 1405-1409, 2011.

      [12] B. Zhang, X. Wu, J. You, Q. Li, & F. Karray, Detection of microaneurysms using multi-scale correlation coefficients, Pattern Recognition, 43(6): 2237-2248, 2010.

      [13] V. S. Hari, V. P. J. Raj, R. Gopikakumari, Quadratic filter for the enhancement of edges in retinal images for the efficient detection and localization of diabetic retinopathy, Formal Pattern Analysis & Applications 1-21, 2015.

      [14] Asadi S, Hassan. H, ‘Detection of Micro aneurysms in Retinal Angiography Image using the circular Hough Transform’, Journal of Advances in computer Research ,vol 3,No 1,2012,pages 1-12

      [15] T. Walter, P. Massin, A. Erginay, R. Ordonez, C. Jeulin, J. Klein, Automatic detection of microaneurysms in color fundus images, Medical Image Analysis 11 (6) (2007) 555–566.

      [16] T.Spencer, R. P. Phillips, P. F. Sharp, J. V. Forrester, Automated detection and quantification of microaneurysms in fluorescein angiograms, Graefes Archive for Clinical & Experimental Ophthalmology, 230(230): 36-41, 1992.

      [17] M. Niemeijer, G. B. Van, J. Staal, M. S. Suttorp-Schulten, M. D. Abràmoff, Automatic detection of red lesions in digital color fundus photographs, IEEE Transactions on Medical Imaging, 24(5): 584-92, 2005.

      [18] A. D. Fleming, S. Philip, K. A. Goatman, J. A. Olson, P. F. Sharp, Automated microaneurysm detection using local contrast normalization and local vessel detection, IEEE Transactions on Medical Imaging, 25(9): 1223-32, 2006.

      [19] A. Mizutani, C. Muramatsu, Y. Hatanaka, S. Suemori, T. Hara, H. Fujita .Automated microaneurysm detection method based on double ring filter in retinal fundus images. (2009), p. 72601N

      [20] Sopharak, A., Uyyanonvara, B., Barman, S.: Automatic microaneurysm quantification for diabetic retinopathy screening. In: Proceedings of World Academy of Science, Engineering and Technology, p. 1722 (2013)

      [21] Sopharak, A., Uyyanonvara, B., Barman, S.: Simple hybrid method for fine microaneurysm detection from non-dilated diabetic retinopathy retinal images. Comput. Med. Imag. Graph. 37(5), 394–402 (2013)

      [22] T. Inoue, Y. Hatanaka, S. Okumura, C.Muramatsu, H. FujitaAutomated microaneurysm detection method based on eigenvalue analysis using hessian matrix in retinal fundus images Engineering in medicine and biology society (EMBC), 2013 35th annual internationalconference of the IEEE (2013), pp. 5873-5876

      [23] ConfProc IEEE Eng Med Biol Soc. 2013; 2013:5873-6. doi: 10.1109/EMBC.2013.6610888. ‘Automated microaneurysm detection method based on Eigenvalue analysis using Hessian matrix in retinal fundus images’.

      [24] R. J. Winder, P. J. Morrow, I. N. McRitchie, J. R. Bailie, and P. M. Hart, ‘Algorithms for digital image processing in diabetic retinopathy’, Comput. Med. Imaging Graph. vol. 33, no. 8, pp. 608–622, 2009.

      [25] University of Iowa. (2007) Retinopathy Online Challenge. [Online].http://webeye.ophth.uiowa.edu/ROC/var.1/www/.


 

View

Download

Article ID: 16573
 
DOI: 10.14419/ijet.v7i2.25.16573




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.