Journal of Digital Imaging

, Volume 24, Issue 3, pp 394–404 | Cite as

A Study on Hemorrhage Detection Using Hybrid Method in Fundus Images

  • Jang Pyo Bae
  • Kwang Gi Kim
  • Ho Chul Kang
  • Chang Bu Jeong
  • Kyu Hyung Park
  • Jeong-Min Hwang
Article

Abstract

Image processing of a fundus image is performed for the early detection of diabetic retinopathy. Recently, several studies have proposed that the use of a morphological filter may help extract hemorrhages from the fundus image; however, extraction of hemorrhages using template matching with templates of various shapes has not been reported. In our study, we applied hue saturation value brightness correction and contrast-limited adaptive histogram equalization to fundus images. Then, using template matching with normalized cross-correlation, the candidate hemorrhages were extracted. Region growing thereafter reconstructed the shape of the hemorrhages which enabled us to calculate the size of the hemorrhages. To reduce the number of false positives, compactness and the ratio of bounding boxes were used. We also used the 5 × 5 kernel value of the hemorrhage and a foveal filter as other methods of false positive reduction in our study. In addition, we analyzed the cause of false positive (FP) and false negative in the detection of retinal hemorrhage. Combining template matching in various ways, our program achieved a sensitivity of 85% at 4.0 FPs per image. The result of our research may help the clinician in the diagnosis of diabetic retinopathy and might be a useful tool for early detection of diabetic retinopathy progression especially in the telemedicine.

Key words

Template matching hemorrhage fundus image 

References

  1. 1.
    The 4th Korea National Health & Nutrition Examination Survey, Ministry of Welfare and Family Affairs, South Korea, 2008Google Scholar
  2. 2.
    Klein R, Klein B, Moss S, Davis M, DeMets D: The Wisconsin epidemiologic study of diabetic retinopathy. Prevalence and risk of diabetic retinopathy when age at diagnosis is less than 30 years. Arch Ophthalmol 102(4):520–526, 1984PubMedGoogle Scholar
  3. 3.
    Patton N, Aslam TM, MacGillivary T, Deary IJ, Dhillon B, Eikelboom RH, Yogesan K, Constable IJ: Retinal image analysis: Concepts, applications and potential. Progress Retinal Eye Res 25:99–127, 2006CrossRefGoogle Scholar
  4. 4.
    Spencer T, Olson J, McHardy K, Sharp P, Forrester J: An image-processing strategy for the segmentation and quantification in fluorescein angiograms of the ocular fundus. Comput Biomed Res 29:284–302, 1996PubMedCrossRefGoogle Scholar
  5. 5.
    Frame A, Undrill P, Cree M, Olson J, McHardy K, Sharp P, Forrester J: A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms. Comput Biol Med 28:225–231, 1998PubMedCrossRefGoogle Scholar
  6. 6.
    Niemeijer M, Ginneken BV, Staal J, Suttorp-Schulten MS, Abramoff MD: Automatic detection of red lesions in digital color fundus photographs. IEEE Trans Medical Imaging 24(5):584–592, 2005CrossRefGoogle Scholar
  7. 7.
    Fleming AD, Philip S, Goatman KA, et al: Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Trans in Medical Imaging 25(9):1223–1232, 2006CrossRefGoogle Scholar
  8. 8.
    Quellec G, Lamard M, Josselin PM, Cazuguel G, Cochener B, Roux C (2008) Optimal wavelet transform for the detection of microaneurysms in retina photographs. IEEE Trans Med 27(9):1230–1241CrossRefGoogle Scholar
  9. 9.
    Hatanaka Y, Nakagawa T, Hayashi Y, Hara T, Fujita H: Improvement of Automated Detection Method of Hemorrhages in Fundus Images. IEEE EMBS Vancouver, Canada, 2008, pp 5429–5432Google Scholar
  10. 10.
    Abramoff MD, Viergever MA, Niemeijer M, Russell SR, Suttorp-Schulten MSA, van Ginneken B (2008) Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes. Diabetes Care 31(2):193–198PubMedCrossRefGoogle Scholar
  11. 11.
    Suri JS, Wilson DL, Laxminarayan S: Handbook of biomedical image analysis II:320–323, 2005Google Scholar
  12. 12.
    Zuiderveld K: Contrast Limited Adaptive Histogram Equalization. San Diego: Academic Press Professional, Inc., 1994Google Scholar
  13. 13.
    Otsu N: A threshold selection method from gray-level histograms. IEEE Trans Sys Man Cyber 9:62–66, 1979CrossRefGoogle Scholar
  14. 14.
    Heucke L, Knaak M, Orglmeister R: A new image segmentation method based on human brightness perception and foveal adaptation. IEEE Signal Processing Letters 7(6):129–131, 2000CrossRefGoogle Scholar
  15. 15.
    Zhang X, Chutatape O: A SVM approach for detection of hemorrhages in background diabetic retinopathy. Proceedings of 2005 IEEE International Joint Conference on Neural Networks, 2005. IJCNN ’05Google Scholar
  16. 16.
    Sinthanayothin C, Boyce JF, Williamson TH, Cook HK, Mensah E, Lal S, Usher D: Automatic detection of diabetic retinopathy on digital fundus images. Diabetic Med 19(2):105–112, 2002PubMedCrossRefGoogle Scholar
  17. 17.
    Bouhaimed M, Gibbins R, Owens D: Automated detection of diabetic retinopathy: Results of a screening study. Diabetes Technol Ther 10(2):142–148, 2008PubMedCrossRefGoogle Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2010

Authors and Affiliations

  • Jang Pyo Bae
    • 1
  • Kwang Gi Kim
    • 1
  • Ho Chul Kang
    • 1
  • Chang Bu Jeong
    • 1
  • Kyu Hyung Park
    • 2
  • Jeong-Min Hwang
    • 2
  1. 1.Biomedical Engineering Branch, Division of Basic & Applied SciencesNational Cancer CenterGoyang-siSouth Korea
  2. 2.Department of OphthalmologySeoul National University College of Medicine, Seoul National University Bundang HospitalSeongnamSouth Korea

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