Automatic Detection of Proliferative Diabetic Retinopathy With Hybrid Feature Extraction Based on Scale Space Analysis and Tracking

Wilda Imama Sabilla, Rully Soelaiman, Chastine Fatichah


Feature extraction is a process to obtain the characteristics or features of an object where the value of the features will be used for analysis in the next process. In retinal image, extraction of blood vessels’ characteristics can be used for detection of proliferative diabetic retinopathy (PDR). Retinal blood vessels’ features can be obtained directly with segmented image and with additional spatial method. For PDR detection, we need the suitable method that can produce maximum feature representation. This paper proposed hybrid feature extraction using a scale space analysis method and tracking with Bayesian probability. The result of the retinal images classification from STARE database using soft threshold m-Mediods classifier shows the best accuracy of 98.1%.


Feature extraction; soft threshold m-Mediods; proliferative diabetic retinopathy; retinal blood vessel segmentation; scale space analysis; tracking

Full Text:



Wilda Imama Sabilla, Chastine Fatichah, Rully Soelaiman, “Implementasi Pengklasifikasi Segmen Vaskular Retina Mata dengan Metode M-Mediods Multivariat”, JURNAL TEKNIK POMITS Vol. 2, No. 1, 2014.

Yi Yin, Mouloud Adel, Salah Bourennane, “Retinal vessel segmentation using a probabilistic tracking method”, Pattern Recognition, vol. 45, pp. 1235-1244, 2012.

Shehzad Khalid, Anam Tariq, M. Younus Javed M. Usman Akram, “Detection of neovascularization in retinal images using multivariate m-Mediods based classifier,” Journal Computerized Medical Imaging and Graphics, vol. 37, no. 5-6, pp. 346-357, September 2013.

R.A. Welikala, J. Dehmeshki, A. Hoppe, V. Tah, S. Mann, T.H. Williamson, S.A. Barman, “Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification”, Computer Methods and Programs in Biomedicine, vol. 114, pp. 247–261, May 2014.

Israr Ul Haq, Usman Akram, Yoshifumi Saijo, “Automated Detection of New vessels for Classification of Proliferative Diabetic Retinopathy”, the 2nd International Conference on Intelligent Systems and Image Processing 2014 (ICISIP2014), pp. 35-38, 2014.



  • There are currently no refbacks.

View my Stat: Click Here

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.