Segmentation Based on Gabor Transformation with Machine Learning: Modeling of Retinal Blood Vessels System from RetCam Images and Tortuosity Extraction

摘要

In a field of the clinical ophthalmology, an analysis of the retinal blood vessels is one of the major assessments in the retinal system. Retinal blood vessels system is clinically imagined either by the fundus camera or retinal probe (RetCam 3 system). The tortuosity is important parameter assessing blood vessel curvature. Unfortunately, this parameter is usually subjectively estimated in the retinal image analysis. The main aim of the analysis is an automatic segmentation with consequent extraction and modelling of the retinal blood vessels system from RetCam 3 in the form of the binary model. Segmentation algorithm utilizes the Gabor wavelet transformation (GT) giving segmentation results for individual parameters setting. Consequent retinal blood vessels classification is carried out on the base of the linear regression with gold standard. The gold standard represents a manually labelled segmentation by the ophthalmologic experts. Binary segmentation model precisely approximates blood vessels area from other structures. This model allows for the tortuosity extraction in a form of the gradient image where each blood vessel element is described by its steepness.