Jorge Silva

Abstract

Abstract
The choroid is the middle layer of the eye globe located between the retina and the sclera. It is proven that choroidal thickness is a sign of multiple eye diseases. Optical Coherence Tomography (OCT) is an imaging technique that allows the visualization of tomographic images of near surface tissues like those in the eye globe. The automatic calculation of the choroidal thickness reduces the subjectivity of manual image analysis as well as the time of large scale measurements. In this paper, a method for the automatic estimation of the choroidal thickness from OCT images is presented. The pre-processing of the images is focused on noise reduction, shadow removal and contrast adjustment. The inner and outer boundaries of the choroid are delineated sequentially, resorting to a minimum path algorithm supported by new dedicated cost matrices. The choroidal thickness is given by the distance between the two boundaries. The data are then interpolated and mapped to an infrared image of the eye fundus. The method was evaluated by calculating the error as the distance from the automatically estimated boundaries to the boundaries delineated by an ophthalmologist. The error of the automatic segmentation was low and comparable to the differences between manual segmentations from different ophthalmologists. © 2018, Springer International Publishing AG.

Abstract

Abstract
An accurate detection of the macular edema (ME) presence constitutes a crucial ophthalmological issue as it provides useful information for the identification, diagnosis and treatment of different relevant ocular and Systemic diseaseS. serous Retinal Detachment (sRD) is a particular type of ME, which is characterized by the leakage of fluid that has a propensity of being accumulated in the macular region. This paper proposes a new methodology for the automatic identification and characterization of the sRD edema using Optical Coherence Tomography (OCT) imageS. The subretinal fluids and the External Limiting Membrane (ELM) retinal layers are identified and characterized to measure the disease severity. Four different visualization modules were designed including representative derived parameters to facilitate the doctor's work in the diagnostic evaluation of ME. The different steps of this method were validated using the manual labelling provided by an expert clinician. The validation of the proposed method offered satisfactory results, constituting a suitable scenario with intuitive visual representations that also include different relevant biomarkerS. © 2018 The Author(s).

Abstract