Mapping the human retina

Abstract

The new therapeutic method of scotoma-based photocoagulation (SBP) developed at the Vienna Eye Clinic for diagnosis and treatment of age-related macular degeneration requires retinal maps from scanning laser ophthalmoscope images. This paper describes in detail all necessary image analysis steps for map generation. A prototype software system for fully automatic map generation has been implemented and tested on a representative dataset selected from a clinical study with 50 patients. The map required for the SBP treatment can be reliably extracted in all cases. Thus, algorithms presented in this paper should be directly applicable in daily clinical routine without major modifications.

Image registration and subtraction for the visualization of change in diabetic retinopathy screening

“…These techniques have tended to focus on two main objectives, the automatic localization of the anatomy of the eye and the detection of specific pathology. Several techniques have been developed for the detection of the optic disk, fovea and vascular tree [16][17][18]. The localization of specific features, such as blood vessels, is generally a prerequisite step in differentiating lesions from the normal anatomy of the eye.…”

Robust rigid registration of retinal angiograms through optimization

“…In the majority of the previously published works on automatic registration of the retinal images [2,5,10,17,21,23,25,27], it was assumed that the extraction of the features or the landmarks are known a priori. However, in late phase, the retinal angiogram images are characterized by poor local contrast.…”

Detection of glaucomatous change based on vessel shape analysis

“…These methods for vessel segmentation in images of the retina can be divided into two different groups: the rule-based methods and the supervised methods. The rule-based methods comprise vessel tracking [4][5][6], matched filter responses [7][8][9], grouping of edge pixels [10], model based locally adaptive thresholding [11], topology adaptive snakes [12] and morphology-based techniques [13,14]. The supervised methods are ridge-based [15], neural-based [16] and wavelet-based feature extraction [17].…”