Context- Vision is one of the five elementary physiologial senses. Vision is enabled via the eye, a very delicate sense organ which is highly susceptible to damage which results in loss of vision. The damage comes in the form of injuries or diseases such as diabetic retinopathy and glaucoma. While it is not possible to predict accidents, predicting the onset of disease in the earliest stages is highly attainable. Owing to the leaps in imaging technology,it is also possible to provide near instant diagnosis by utilizing computer vision and image processing capabilities.

Objectives- In this thesis, an algorithm is proposed and implemented to classify images of the retina into healthy or two classes of unhealthy images, i.e, diabetic retinopathy, and glaucoma thus aiding diagnosis. Additionally the algorithm is studied to investigate which image transformation is more feasible in implementation within the scope of this algorithm and which region of retina helps in accurate diagnosis.

Methods- An experiment has been designed to facilitate the development of the algorithm. The algorithm is developed in such a way that it can accept all the values of a dataset concurrently and perform both the domain transforms independent of each other.

Results- It is found that blood vessels help best in predicting disease associations, with the classifier giving an accuracy of 0.93 and a Cohen’s kappa score of 0.90. Frequency transformed images also presented a accuracy in prediction with 0.93 on blood vessel images and 0.87 on optic disk images.

Conclusions- It is concluded that blood vessels from the fundus images after frequency transformation gives the highest accuracy for the algorithm developed when the algorithm is using a bag of visual words and an image category classifier model.

Keywords-Image Processing, Machine Learning, Medical Imaging