A Novel Technique for Splat Generation and Patch Level Prediction in Diabetic Retinopathy

Abstract

Diabetic Retinopathy (DR) is vision threatening and can be prevented with early diagnosis and treatment. This can be achieved with the regular screening of patients known to have Diabetes for 5 years or more. Once detected with DR, it is important for doctors to maintain the progress of the disease down the line. This includes identification and marking of DR features in the fundus images. Manual marking of DR features like exudates and hemorrhages is tedious and error prone job for opthalmologists. Detection of DR is widely done with fundus imaging technique. To help aid ophthalmologists, the DR features in fundus images can be automatically marked using machine learning algorithms [1]. In this paper, a novel and generalized method for segmenting the fundus images is proposed. With our approach, retinal color images are partitioned into non-overlapping segments covering the entire image. Each segment, i.e., splat, contains pixels with similar color and spatial location. A novel method for automated generalised generation of splat is presented in this paper and further marking of the diseased splats is also proposed. The proposed method is tested on DIARETDB1 dataset, achieving accuracies of 87.7% for exudate patches, 84.6% for hemorrhage patches and 80.7% for normal patches.