Biologically-inspired supervised vasculature segmentation in SLO retinal fundus images

We propose a novel Brain-Inspired Multi-Scales and Multi-Orientations (BIMSO) segmentation technique for the retinal images taken with laser ophthalmoscope (SLO) imaging cameras. Conventional retinal segmentation methods have been designed mainly for color RGB images and they often fail in segmenting the SLO images because of the presence of noise in these images. We suppress the noise and enhance the blood vessels by lifting the 2D image to a joint space of positions and orientations (SE(2)) using the directional anisotropic wavelets. Then a neural network classifier is trained and tested using several features including the intensity of pixels, filter response to the wavelet and multi-scale left-invariant Gaussian derivatives jet in SE(2). BIMSO is robust against noise, non-uniform luminosity and contrast variability. In addition to preserving the connections, it has higher sensitivity and detects the small vessels better compared to state-of-the-art methods for both RGB and SLO images

Automatic detection of vascular bifurcations and crossings in retinal images using orientation scores

Several ocular and systemic diseases such as hypertension and arteriosclerosis cause geometrical and functional changes to the vasculature in retinal images, including alterations in the shape of vascular bifurcations and crossings. To use the diagnostic information of the junctions, it is important to detect them first. In this work, a novel BIfurcation and CRossing detection method using Orientations Scores (BICROS) is introduced. The Brain-inspired orientation score transformation lifts the image to the joint space of positions and orientations using directional anisotropic wavelets. Candidate junctions are selected based on their geometrical properties in this space. Then false detections are eliminated in a supervised manner. Additionally, a more conventional pipeline for junction detection based on morphological analysis of vessel segmentations is included. Finally, both approaches are combined and the resulting junctions are classified into bifurcations and crossings. The proposed method outperforms state of the art on a public and private dataset