Segmentation of Retinal Features Using Hybrid BINI Thresholding in Diabetic Retinopathy Fundus Images

  • R. ShaliniEmail author
  • S. Sasikala
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1118)


In the recent era, the increasing diabetic ratio effects in producing complex retinal diseases. The early diagnosis will avoid the consequences of diabetic-related complications. So, there is an urge to develop a computerized model for identifying the retinopathy features caused due to diabetes while evading the false positives. The aim of this paper is to identify and segment the retinal features like blood vessels, optic disc in diabetic retinopathy (DR) fundus images in order to remove the non-diabetic retinopathy features which makes the detection of DR features (lesions) easier. And this is done by using a hybrid segmentation algorithm called BINI which combines the model of both binary and Niblack’s thresholding. Initially, the input image is standardized by resizing the image using bi-cubic interpolation area method. Then, the fundus image quality is enhanced using preprocessing techniques like green channel extraction, intensity channel extraction, median filtering, contrast limited adaptive histogram equalization and morphological operations. The preprocessed images are segmented to produce retinal features using the BINI algorithm. It is a novel method which hybrids both binary thresholding and Niblack’s thresholding. The performance of the segmentation methods is evaluated using the validation measures like Rand index (Ri) and Jaccard index (Ji), Precision (Pr), Recall (Rc) and F-Measure (Fm). The proposed method for segmenting the retinal features using BINI thresholding has given an accuracy of about 96.48%; it leads to an accuracy of 100% clear segmentation of the lesions of diabetic retinopathy images.


Diabetic retinopathy Blood vessels Optic disc Retinal BINI 


  1. 1.
    Anupama Pattanashetty., Suvarna Nandyal.: Diabetic retinopathy detection using image processing: a survey. Int. J. Comput. Sci. Netw. 661–666 (2016)Google Scholar
  2. 2.
    Shalini, R., Sasikala, S.: Segmentation of hard exudates using fuzzy-C-means in diabetic retinopathy fundus images. In: International Conference on Intelligent Computing and Control Systems [ICICCS] (2019)Google Scholar
  3. 3.
  4. 4.
  5. 5.
    Shalini, R., Sasikala, S.: A survey on detection of diabetic retinopathy. IEEE,, pp. 626–630 (2018)
  6. 6.
    Birendra Biswal., Thotakura Pooja., Bala Subrahmanyam, N.: Robust retinal blood vessel segmentation using line detectors with multiple masks. IET J. The Institution of Engineering and Technology, pp. 389–399 (2018)Google Scholar
  7. 7.
    Bandara, A.M.R.R., Giragama, P.W.G.R.M.P.B.: A retinal image enhancement technique for blood vessel segmentation algorithm. ICIIS-IEEE, pp. 1–5 (2017)Google Scholar
  8. 8.
    Sheetal Maruti Chougule., Renke, A.L.: New preprocessing approach for images in diabetic retinopathy screening. Int. J. Eng. Res. Technol. 501–503 (2017)Google Scholar
  9. 9.
    Wahyudi Setiawan., Mohammad Imam Utoyo., Riries Rulaningtyas.: Retinal vessel segmentation using a modified morphology process and global thresholding. In: The 8th Annual Basic Science International Conference, pp. 060031(1)–060031(10) (2018)Google Scholar
  10. 10.
    Ching-Lin Wang., Ming-Yuan Hsieh., Yi-Wen Hung., Meng-Hsiun Tsai., Mao-Hsiang Chan., Jui-Ming Chen., Kwong-Chung Tung.: Retina image-based optic disc segmentation. Adv. Mech. Eng. 1–9 (2016)Google Scholar
  11. 11.
    Jyothiprava Dash., Nilamani Bhoi.: A thresholding based technique to extract retinal blood vessels from fundus images. Fut. Comput. Inf. J. 2, 103–109 (2017) (ScienceDirect)Google Scholar
  12. 12.
    Gehad Hassan., Nashwa El-Bendary., Aboul Ella Hassanien, Ali Fahmy., Abullah M.Shoeb., Vaclav Snasel.: Retinal blood vessel segmentation approach based on mathematical morphology. In: International Conference on Communication, Management and Information Technology (ICCMIT 2015), ScienceDirect, pp. 612–622 (2015)Google Scholar
  13. 13.
    Senthilkumaran, N., Kirubakaran, C.: Efficient implementation of Niblack thresholding for MRI brain image segmentation. Int. J. Comput. Sci. Inf. Technol. 2173–2176 (2014)Google Scholar
  14. 14.
    Nidhal Khdhair El Abbadi., Enas Hamood Al Saadi.: Blood vessels extraction using mathematical morphology. J. Comput. Sci. 1389–1395 (2013)Google Scholar
  15. 15.
    Siva Sundhara Raja, D., Vasuki, S.: Automatic detection of blood vessels in retinal images for diabetic retinopathy diagnosis. Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine, pp. 1–12 (2014)Google Scholar
  16. 16.
    Jayalakshmi, N., Priya, K.: A review on retinal feature segmentation methodologies for diabetic retinopathy. IOSR J. Comput. Eng. 1–6 (2017)Google Scholar
  17. 17.
  18. 18.
  19. 19.
  20. 20.
  21. 21.

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceIDE, University of MadrasChennaiIndia

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