Segmentation of retinal vessels in adaptive optics images for assessment of vasculitis

Abstract

International audience—In this paper we propose a new method for segmenting retinal vessels in adaptive optics images. This method is particularly dedicated for segmenting vessels with significant morphological alterations due to vasculitis, but it is also accurate for vessels with moderate or without alteration. It relies on a pre-segmentation step which is crucial for the robustness and accuracy of the results. This step is based on a specific morphological processing of isolines of the original image: they constitute of good basis for the segmentation because they are disposed along the wall borders of the vessels. Regularization is then performed using active contour model embedding a parallelism constraint. This novel model allows precise segmenting inner and outer walls of the vessel. In particular it is more accurate in the case of vasculitis than the existing methods. This is the only method that allows quantification. The results and the runtime make it suitable for clinical use

Performance Analysis of Adaptive NEBF Filter Using Cellular Automata for Retinal Vessel Segmentation

“…Some sort of consecutive convolution cellular levels and also pooling cellular levels keep to the input data, so your multilevel can certainly find out the fuzy features so that you can segment retinal vessel. Rossant et al [11] has presented the approach which is committed by using substantial improvements resulting from vasculitis, however it is as well genuine for vessel segmentation by using average need of alteration. That uses a presegmentation measure which is essential for any robustness in addition to accuracy and reliability of your results.…”