An Automated Approach to Differentiate a Normal eye from a Defective Eye

Detection of basic differentiating characteristics of eye diseases from the images of the retina can be a good approach as a low-cost method for broad-based initial screening. For example early diabetic retinopathy detection enables application of laser therapy treatment in order to prevent or delay loss of vision. The paper has referenced Diabetic retinopathy and Retinitis pigmentosa for analysis. Automated approach for detection of microaneurysms in digital color fundus photographs helps ophthalmologist to detect the emergence of its initial symptoms and determine the next action step for the patient. A similar mechanism for automated early disease detection method with respect to the features of the normal eye is proposed. The detection algorithm features identification of black pigments like minute features, microaneurysm and exudate detection and these features extracted can prove to a greater extent as ready instances for defectiveness. A number of images along with the feedback and consultation from the ophthalmologist in this area of medical science has proved to be a great help towards the observation as derived from this mechanism and discussed in the later end of this paper. The proposed mechanism can be extended up to the limit of supervised learning so as to automate the practical feedbacks as obtained from the practitioners.