Abstract
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.
Highlights
Optical coherence tomography (OCT) has nowadays become an established biomedical imaging modality [1]
A promising functional extension of OCT is Doppler OCT (DOCT) [1], which might help for early diagnosis of major retinal diseases as perfusion is highly affected by pathologies
Before processing the DOCT images, we corrected the images for background motion by using a histogram based algorithm [11]
Summary
Optical coherence tomography (OCT) has nowadays become an established biomedical imaging modality [1]. The transition from time-domain to Fourier-domain OCT enabled the imaging of full 3D volumes with high resolution [2,3,4,5]. Quantitative information is provided for example by phase resolved DOCT which extracts flow information by measuring the phase shift between adjacent A-scans [17,18]. This induced phase shift is related to the axial velocity component of moving scatterers, such as red blood cells. In phase difference or Doppler tomograms, Doppler flow is mapped to a different phase difference range than bulk tissue that exhibits no motion It can, in principle be segmented from the image histogram [21]. Resonant Doppler imaging used the effect of fringe washout for segmentation of moving structures from bulk tissue [35]
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