Segmentation of Doppler optical coherence tomography signatures using a support-vector machine

When processing Doppler optical coherence tomography images, there is a need to segment the Doppler signatures of the vessels. This can be used for visualization, for finding the center point of the flow areas or to facilitate the quantitative analysis of the vessel flow. We propose the use of a support-vector machine classifier in order to segment the flow. It uses the phase values of the Doppler image as well as texture information. We show that superior results compared to conventional simple threshold-based methods can be achieved in conditions of significant phase noise, which inhibit the use of a simple threshold of the phase values.