BOUNDARY SEGMENTATION AND DETECTION OF DIABETIC RETINOPATHY (DR) IN FUNDUS IMAGE

Authors

  • R. Samad Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia
  • M. S. F. Nasarudin Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia
  • M. Mustafa Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia
  • D. Pebrianti Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia
  • N. R. H. Abdullah Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia

DOI:

https://doi.org/10.11113/jt.v77.6222

Keywords:

Segmentation, diabetic retinopathy, fundus image, Fuzzy C-Means

Abstract

Recently, the automatic detection system or Computer-Aided Detection (CAD) is widely developed in the medical field to screen or diagnose the medical image. This paper presents the boundary segmentation and detection of Diabetic Retinopathy (DR) in fundus image. The proposed method uses Fuzzy C-Means for clustering and detect the boundary of the DR object. The number of cluster used in this work is 3 and the average number of iterations is 28.The DR region is successfully detected by FCM and the average processing time is 1.235s.  

References

National Eye Institute. [Online].From:

https://nei.nih.gov/health/diabetic/retinopathy

Wikipedia. [Online]. From:

https://en.wikipedia.org/wiki/Fundus_photography

Castellino, R. A. 2005. Computer-Aided Detection: an overview.Journal of Cancer Imaging. 5(1): 17–19.

Ahmed, M. N., Yamany, S. M., Mohamed, N., Farag, A. A., and Moriarty, T. 2003. A Modified Fuzzy C-Means Algorithm for Bias Field Estimation and Segmentation of MRI Data. IEEE Transactions on Medical Imaging. 21 (3): 193–199.

Feijoo, J. G., Martinez de la Casa, J. M., Serv, H. M., Zamorano, M. R., Bachiller, M. and Suarez, E. J. C. 2008. Digital Retinal Image Database-DRION-DB. [Online]. From:

http://www.ia.uned.es/~ejcarmona/DRIONS-DB.html

Lim, Y. W. and Lee, S. U. 1990. On the Color Image Segmentation Algorithm Based on the Thresholding and the Fuzzy C-Means Techniques. Journal of Pattern Recognition. 23(9): 935-952

Zhang, D. Q. and Chen, S. 2004. A Nov Kernelized Fuzzy C-Means Algorithm with Application in Medical Image Segmentation. Journal of Artificial Intelligence in Medicine. 32(1): 37-50

Fuzzy C Means Clustering. [Online]. From :

http://home.deib.polimi.it/matteucc/Clustering/tutorial_html/cmeans.html

Gonzalez, R. C., Woods, R.E. and Eddins, S. L. 2004. Digital Image Processing using MATLAB. New Jersey: Pearson Prentice Hall

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Published

2015-11-11

Issue

Vol. 77 No. 6: Medical Engineering Vol. 1

Section

Science and Engineering

License

Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.

How to Cite

BOUNDARY SEGMENTATION AND DETECTION OF DIABETIC RETINOPATHY (DR) IN FUNDUS IMAGE. (2015). Jurnal Teknologi (Sciences & Engineering), 77(6). https://doi.org/10.11113/jt.v77.6222