ABSTRACT
Diabetic macular edema (DME) is a leading cause of vision loss in patients with diabetes. Computer-aided diagnostic (CAD) systems enable automated detection of ophthalmic pathological sites, monitoring the progression of pathology, and can guide follow-up treatment processes. Optical Coherence Tomography (OCT) images have been widely used to assess macular diseases, and they have enabled analysis of the extent of disorganization in the sub-retinal layers due to DME. This chapter presents a CAD system that denoises and segments seven sub-retinal surfaces and six sub-retinal layers in the retinal microstructure for normal OCT images from healthy patients and in abnormal images from patients with DME in less than 35 s per image. The proposed automated OCT segmentation algorithm involves two key steps. In the first step, additive noise introduced by the imaging systems is removed from the images. In the second step, the denoised images are subjected to model-based segmentation. he proposed denoising method.
