Automated lumen definition from 30 MHz intravascular ultrasound images

One prerequisite for standard clinical use of intravascular ultrasound imaging is rapid evaluation of the data. The main quantities to be extracted from the data are the size and the shape of the lumen. Until now, no accurate, robust and reproducible method to obtain the lumen boundaries from intravascular ultrasound images has been described. In this study, 21 different (semi-)automated binary-segmentation methods for determining the lumen are compared with manual segmentation to find an alternative for the laborious and subjective procedure of manual editing. After a preprocessing step in which the catheter area is filled with lumen-like grey values, all approaches consist of two steps: (i) smoothing the images with different filtering methods and (ii) extracting the lumen by an object definition method. The combination of different filtering methods and object definition methods results in a total of 21 methods and 80 experiments. The results are compared with a reference image, obtained from manual editing, by use of four different quality parameters--two based on squared distances and two based on Mahalanobis distances. The evaluation has been carried out on 15 images, of which seven are obtained before balloon dilation and eight after balloon dilation. While for the post-dilation images no definite conclusions can be drawn, an automated contour model applied to images smoothed with a large kernel appears to be a good alternative to manual contouring. For pre-dilation images a fully automated active contour model, initialized by thresholding, preceded by filtering with a small-scale median filter is the best alternative for manual delineation. The results of this method are even better than manual segmentation, i.e. they are consistently closer to the reference image than the average distance of all individual manual segmentations.