Author Topic: Exudates Detection Methods in Retinal Images Using Image Processing Techniques  (Read 3312 times)

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Author : V.Vijayakumari, N. Suriyanarayanan
International Journal of Scientific & Engineering Research, Volume 1, Issue 2, November-2010
ISSN 2229-5518

India and China are, and will remain, the leading coun-tries in terms of the number of people with diabetes mellitus in the year 2025. Among the 10 leading coun-tries in this respect, five are in Asia. Although only a moderate increase in the total population in China is expected in the next 25 years, China is estimated to contribute almost 38 million people to the global burden of diabetes in the year 2025. India, due to its immense population size and high diabetes prevalence, will contribute 57 million [1]and [2].  These figures are based on estimated population growth, population ageing, and urbanization, but they do not take into account changes in other diabetes-related risk factors.

So, Diabetic screening programmes are necessary in addressing all of these factors when working to eradicate preventable vision loss in diabetic patients. When performing retinal screening for Diabetic Retinopathy [3] some of these clinical presentations are expected to be imaged. Diabetic retinopathy is globally the primary cause of blindness not because, it has the highest incidence and it often remains undetected until severe vision loss occurs. Advances in shape analysis, the development of strategies for the detection and quantitative characterization of blood vessel changes in the retina are of great importance. Automated early detection of the presence of exudates can assist the oph-thalmologists to prevent the spread of disease more efficiently.

Direct digital image acquisition using fundus cameras combined with image processing and analysis techniques has the potential to enable automated diabetic retinopathy screening. The normal features of fundus images include optic disk, fovea and blood vessels. Exudates and haemorrhages are the main abnormal features which is the leading cause of blindness in the working age population.
Optic disk is the brightest [4] part in the normal fundus images which can be seen as a pale, round or vertically slightly oval disk. Finding the main components in the fundus images helps in characterizing detected lesions and in identifying false positives. Abnormality detection in images is found to play an important role in many real life applications [5] suggested  neural network approach for the detection and classification of exudates. A decision support frame work for deducing the presence or absence of DR are developed and tested [6]. The detection rule is based on binary-hypothesis testing problem which simplifies the problem to yes/no de-cisions. The results suggest that by biasing the classifier towards DR detection, it is possible to make the classifier achieve good sensitivity.

2.1 Feature Extration
Here, in this method we use the concept that in normal retinal images the optic disc is the brightest part and next to it comes the exudates. So once after detecting the optic disc, the centre point is determined for extraction of vari-ous features in the image. Then the optic disc is removed from the image, thus we are now left with exudates as the next brightest region. Here again we can apply Binary Image [7] and proper threshold value is set and the exudates can be easily identified from the test image. The results are shown in figures 1 and 2.

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2.2 Template Matching
For The concept behind this method is that, a normal and healthy retinal image is taken and it is kept as the refer-ence to isolate the abnormalities in the test image. This reference image acts as the template. Both the reference image and test images are converted from RGB to GRAY levels and then pixel by pixel both the images are com-pared. During comparison, the additional objects present in the test image get isolated and they are clearly visible in the output. If the test image is normal, then while com-parison it gets cancelled as there is no difference of pixel value between the two, where as in the test image with exudates, the optic disc gets cancelled and only exudates are separated in the output. and is shown in figure 3 to 5

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2.3   Minimum Distance Discriminant Classifier
Color information has shown to be effective for le-sions detection under certain conditions. On the basis of color information, the presence of lesions can be preliminarily detected by using MDD (Minimum Distance Discriminant) classifier based on statistical pattern recognition techniques.

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2.4   Enhanced MDD Classifier
This image works on the RGB co-ordinates rather than spherical co-ordinates. In the Minimum Dis-tance Discriminant (MDD) Classifier method, the centre of class is found using a training set and hence remains fixed. But this may cause problem because of difference in image illumination and their average intensity. So a method is employed such that the centre of class (Cyell and Cbgnd) varies dynamically depending on the image.

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