Robust multimodal registration of fluorescein angiography and optical coherence tomography angiography images using evolutionary algorithms.

Optical coherence tomography angiography (OCTA) and fluorescein angiography (FA) are two different vascular imaging modalities widely used in clinical practice to diagnose and grade different relevant retinal pathologies. Although each of them has its advantages and disadvantages, the joint analysis of the images produced by both techniques to analyze a specific area of the retina is of increasing interest, given that they provide common and complementary visual information. However, in order to facilitate this analysis task, a previous registration of the pair of FA and OCTA images is desirable in order to superimpose their common areas and focus the gaze on the regions of interest.

Fast detection of the main anatomical structures in digital retinal images based on intra- and inter-structure relational knowledge.

Background And Objective: The anatomical structure detection in retinal images is an open problem. However, most of the works in the related literature are oriented to the detection of each structure individually or assume the previous detection of a structure which is used as a reference. The objective of this paper is to obtain simultaneous detection of the main retinal structures (optic disc, macula, network of vessels and vascular bundle) in a fast and robust way.

Identification of the optic nerve head with genetic algorithms.

Objective: This work proposes creating an automatic system to locate and segment the optic nerve head (ONH) in eye fundus photographic images using genetic algorithms.

Methods And Material: Domain knowledge is used to create a set of heuristics that guide the various steps involved in the process. Initially, using an eye fundus colour image as input, a set of hypothesis points was obtained that exhibited geometric properties and intensity levels similar to the ONH contour pixels.