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A Survey of Microaneurysms Detection using Segmentation Techniques in Fundus Images

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Priyanka Powar, C. R. Jadhav
10.5120/ijca2016908311

Priyanka Powar and C R Jadhav. Article: A Survey of Microaneurysms Detection using Segmentation Techniques in Fundus Images. International Journal of Computer Applications 135(1):32-34, February 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Priyanka Powar and C. R. Jadhav},
	title = {Article: A Survey of Microaneurysms Detection using Segmentation Techniques in Fundus Images},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {135},
	number = {1},
	pages = {32-34},
	month = {February},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Diabetic Retinopathy is a major medical problem that causes damage to the eye. A need arises to detect it at an early stage. Since this ailment is symptomless, it can only be a diagnosed by oculist. Currently, the trained eye care specialists are not able to screen the exponential increase in the number of Diabetic Retinopathy patients. An automated Diabetic Retinopathy screening system will enable the detection of lesions accurately, thus helping the ophthalmologists. Microaneurysms are the earliest clinical signs of Diabetic Retinopathy. They are reddish in color and appear as small red spots on the retinal fundus images. Early detection of microaneurysm can help in the early treatment of Diabetic Retinopathy. This paper presents the study and review of various techniques used in detection of microaneurysm from the diabetic retinopathy images. This paper is motivated by need of increasing sensitivity and reducing computational time for detection and classification of microaneurysm from the diabetic retinopathy images. Retina images are obtained from the fundus camera and graded by skilled professionals. However there is considerable shortage of expert observers has encouraged computer assisted monitoring. Evaluation of blood vessels network plays an important task in a variety of medical diagnosis. Manifestations of numerous vascular disorders, such as diabetic retinopathy, depend on detection of the blood vessels network.In this work the fundus RGB image is used for obtaining the traces of blood vessels and areas of blood vessels are used for detection of Diabetic Retinopathy (DR).

References

  1. Spencer, 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.
  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.
  3. Fleming, 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.
  4. Walter, Thomas, et al. "Automatic detection of microaneurysms in color fundus images." Medical image analysis 11.6 (2007): 555-566.
  5. Quellec, Gwénolé, et al. "Optimal wavelet transform for the detection of microaneurysms in retina photographs." Medical Imaging, IEEE Transactions on 27.9 (2008): 1230-1241
  6. Esmaeili, 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.
  7. Fadzil, 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
  8. Niemeijer, 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.
  9. Bae, Jang Pyo, et al. "A study on hemorrhage detection using hybrid method in fundus images." Journal of digital imaging 24.3 (2011): 394-404.
  10. Antal, Bálint, and András Hajdu. "Improving microaneurysm detection in color fundus images by using context-aware approaches." Computerized Medical Imaging and Graphics 37.5 (2013): 403-408.
  11. Akram, 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.
  12. Kade 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.
  13. Sopharak, 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
  14. Tavakoli, 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.
  15. Adal, Kedir M., et al. "Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning." Computer methods and programs in biomedicine 114.1 (2014): 1-10.
  16. .

Keywords

Diabetic Retinopathy, Fundus image, Microaneurysm Detection, Retinal Image, Screening.