29 November 2016 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
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Abstract
Lumen segmentation from clinical intravascular optical coherence tomography (IV-OCT) images has clinical relevance as it provides a full three-dimensional perspective of diseased coronary artery sections. Inaccurate segmentation may occur when there are artifacts in the image, resulting from issues such as inadequate blood clearance. This study proposes a transmittance-based lumen intensity enhancement method that ensures only lumen regions are highlighted. A level-set-based active contour method that utilizes the local speckle distribution properties of the image is then employed to drive an image-specific active contour toward 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 artifact. A method to identify the guide-wire and interpolate the lost lumen segments has been implemented. This approach is fast and can be performed 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.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
Shiju Joseph, Shiju Joseph, Asif Adnan, Asif Adnan, David Adlam, David Adlam, } "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," Journal of Medical Imaging 3(4), 044001 (29 November 2016). https://doi.org/10.1117/1.JMI.3.4.044001 . Submission:
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