Automated seeded region growing algorithm for extraction of cerebral blood vessels from magnetic resonance angiographic data

Abstract

Magnetic resonance angiography (MRA) has currently played a useful clinical role as a noninvasive method of surveying vascular anatomy. In this study, we developed an automated seeded region growing algorithm for extraction of blood vessels from MRA data. In the conventional region growing algorithm, the user must manually place a seed point within a large blood vessel and also input the segmentation threshold. Furthermore, these processes must be repeated with a different set of seeds and threshold until the satisfactory results are obtained. Thus, this method is time-consuming and the results obtained by this method are highly subjective to the user. With our algorithm, binary images were firstly generated by thresholding the original MRA data to roughly obtain the images of blood vessels, and then the skeletons were generated from these images using the thinning algorithm based on the Euclidean distance transformation. Finally, these skeletons were used as the seeds for region growing. Our method could extract blood vessels automatically and stably, and the segmentation leakage could be largely suppressed. In conclusion, our automated seeded region growing algorithm appears to be useful for extracting and displaying blood vessels in a three-dimensional manner.