In the last decade intravascular optical coherence tomography has known a tremendous progress. Its high resolution (5-
10μm) allows coronary plaque characterization, vulnerable plaque assessment, and the guidance of intravascular
interventions. However, one intravascular OCT sequence contains hundreds of frames, and their interpretation requires a
lot of time and energy. Therefore, there is a strong need for automated segmentation algorithms to process this large
amount of data. In this article, we present an automated algorithm to extract lumen contours from images obtained with
intravascular Optical Coherence Tomography (OCT). Unlike existing methods, our algorithm requires no post- or preprocessing
of the image. First, a sliding window is passed on every A-scan to locate the artery tissue, this location being
determined from the largest distribution of the grey level values. Once all the tissue is extracted from the image, every
segmented A-scan is binarized separately. For a single A-scan, the level of amplitude often varies strongly across the
tissue. A global threshold would cause low amplitude parts of the tissue to be considered as belonging to the background.
Our solution is to determine local thresholds for every A-scan. That is, instead of having a single global threshold, we
allow the threshold itself to smoothly vary across the image. Subsequently, on the binarized image the Prewitt mask is
moved from the detected tissue position toward the probe to segment the lumen. The proposed method has been
validated qualitatively on images acquired under different conditions without changing any parameter of the algorithm.
Experimental results show that the proposed method is accurate and robust to extract lumen borders.