Abstract:
Medical systems based on state of the art image processing and pattern recognition techniques are very common now a days. These systems are of prime interest to provide basic
health care facilities to patients and support to doctors. Diabetic maculopathy is one of
the retinal abnormalities in which diabetic patient suffers from severe vision loss due to
affected macula. It affects the central vision of the person and causes total blindness in
severe cases. In this research, an image processing and pattern recognition based system is
propose for automated detection and grading of diabetic maculopathy which will assist the
ophthalmologists in early detection of the disease. The proposed system extracts the macula
from digital retinal image using the vascular structure and optic disc location. It creates a
binary map for possible exudate regions using filter banks and formulates a detailed feature
vector for all regions. A hybrid classifier is propose as an ensemble of Gaussian Mixture
Model (GMM) and Support Vector Machine (SVM) to detect exudates from input retinal
image. Finally, the proposed system grades retinal image in different stages of maculopathy by using macular coordinates and exudates. The statistical analysis and comparative
evaluation of proposed system with previously proposed methods are performed on publicly
available standard retinal image databases using performance parameters such as sensitivity, specificity, positive predictive value and accuracy. The performance improvement of
proposed system is demonstrated by comparing them with recently proposed and published
methods.
