Retinal Disease Identification by Segmentation Techniques in Diabetic Retinopathy

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 10)


Detection of microaneurysms before diabetes increases is an essential stage in diabetic retinopathy (DR) which damages the eye, so it is big medical problem. A need arises to detect it at an early stage. It is not showing any symptoms, so it can only be a diagnosed by oculist. This paper presents the study and review of various techniques used in detection of microaneurysms as well as new approach to increasing sensitivity and reducing computational time for detection and classification of microaneurysms from the diabetic retinopathy images. This new strategy to detect MAs is based on (1) Elimination of nonuniform part of an image and standardize grayscale content of original image. (2) Performed Morphological operations for detection of Region of Interest (ROI) and elimination of blood vessels. (3) To identify real MAs two features extracted where one feature of shape which discriminates normal eye image and abnormal eye image and second feature of texture. So, this increases sensitivity and also availability. So for this whole process of new technique used publically available database called DiaretDB1 database. (4) To discriminate normal and abnormal images different clustering algorithm used.


Image processing Diabetic retinopathy Fundus images Microaneurysms detection Retinal image 


  1. 1.
    Pencer, Timothy, et al. “An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus.” Computers and biomedical research 29.4 (1996): 284–302.Google Scholar
  2. 2.
    Niemeijer, Meindert, et al. “Automatic detection of red lesions in digital color fundus photographs.” Medical Imaging, IEEE Transactions on 24.5 (2005): 584–592.Google Scholar
  3. 3.
    Alter, Thomas, et al. “Automatic detection of microaneurysms in color fundus images.” Medical image analysis 11.6 (2007): 555–566.Google Scholar
  4. 4.
    Uellec, Gwnol, et al. “Optimal wavelet transform for the detection of microaneurysms in retina photographs.” Medical Imaging, IEEE Transactions on 27.9 (2008): 1230–1241.Google Scholar
  5. 5.
    Smaeili, Mahdad, et al. “A new curvelet transform based method for extraction of red lesions in digital color retinal images.” Image Processing (ICIP), 2010 17th IEEE International Conference on. IEEE, 2010.Google Scholar
  6. 6.
    Antal, Blint, and Andrs Hajdu. “Improving microaneurysm detection in color fundus images by using context-aware approaches.” Computerized Medical Imaging and Graphics 37.5 (2013): 403–408.Google Scholar
  7. 7.
    Opharak, Akara, Bunyarit Uyyanonvara, and Sarah Barman. “Simple hybrid method for fine microaneurysm detection from non-dilated diabetic retinopathy retinal images.” Computerized Medical Imaging and Graphics 37.5 (2013): 394–402.Google Scholar
  8. 8.
    Avakoli, Meysam, et al. “A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy.” Pattern Recognition 46.10 (2013): 2740–2753.Google Scholar
  9. 9.
    Dal, Kedir M., et al. Automated detection of microaneurysmsusing scale-adapted blob analysis and semi-supervised learnin.Google Scholar
  10. 10.
  11. 11.
    Leming, Alan D., et al. “Automated microaneurysm detection using local contrast normalization and local vessel detection.” Medical Imaging, IEEE Transactions on 25.9 (2006): 1223–1232.Google Scholar
  12. 12.
    Adzil, MH Ahmad, Lila Iznita Izhar, and Hanung Adi Nugroho. “Determination of foveal avascular zone in diabetic retinopathy digital fundus images.” Computers in biology and medicine 40.7 (2010): 657–664.Google Scholar
  13. 13.
    Iemeijer, Meindert, et al. “Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs.” Medical Imaging, IEEE Transactions on 29.1 (2010): 185–195.Google Scholar
  14. 14.
    Ae, Jang Pyo, et al. “A study on hemorrhage detection using hybrid method in fundus images.” Journal of digital imaging 24.3 (2011): 394–404.Google Scholar
  15. 15.
    Kram, M. Usman, Shehzad Khalid, and Shoab A. Khan. “Identification and classification of microaneurysms for early detection of diabetic retinopathy.” Pattern Recognition 46.1 (2013): 107–116.Google Scholar
  16. 16.
    Aade Mahesh k, “A Survey of Automated Techniques for Retinal Disease Identification in Diabetic Retinopathy”. International Journal of Advancements in Research & Technology, May-2013 ISSN 2278–7763.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer EngineeringD.Y. Patil Institute of Engineering & TechnologyPimpri, PuneIndia

Personalised recommendations