Automatic segmentation of coronary morphology using transmittance based lumen intensity-enhanced intravascular optical coherence tomography images and applying a localized level-set based active contour method

Lumen segmentation from clinical intravascular optical coherence tomography (IV-OCT) images has clinical relevance as it provides a full three dimensional (3D) perspective of diseased coronary artery sections. Inaccurate segmentation may occur when there are artefacts in the image, resulting from issues such as inadequate blood clearance. This study proposes a transmittance based lumen intensity enhancement method that ensures lumen regions only are highlighted. A level-set based active contour method (ACM) that utilizes the local speckle distribution properties of the image, is then employed to drive an imagespecific active contour towards the true lumen boundaries. By utilizing local speckle properties, the intensity variation issues within the image are resolved. This combined approach has been successfully applied to challenging clinical IV-OCT datasets that contains multiple lumens, residual blood flow and its shadowing artefact. A method to identify the guide-wire and interpolate the lost lumen segments has been implemented. This approach is fast and can perform even when guide-wire boundaries are not easily identified. Lumen enhancement also makes it easy to identify vessel side branches. This automated approach is not only able to extract the arterial lumen, but also the smaller microvascular lumens that are associated with the vasa vasorum and with atherosclerotic plaque