A New Approach for Automatic Segmentation of LSCM Blood Vessel Images of Time Sequence Based on Region Growing

In the research of drug delivery, the ratio of the extravascular fluorescence intensity to the intravascular fluorescence intensity with respect to a transformed time axis plays an important role. Therefore, it is critical to accurately segment the blood vessel structures from time sequence images of LSCM. Conventionally, the users have to place some seed points into the images based on the region growing method. In this paper, the seed points are automatically taken from itself. The new approach also reduces the range of region growing in the images based on morphology and the computation speed becomes faster. In fact, it utilizes the correlative information between the images which are imaged at adjacent selected time-points during the time-course of experiment. Moreover, edge constraint is combined with region growing to reduce the influence of noisy boundaries and holes within the object. By conducting some experiments of live mice, it is demonstrated that the new approach is able to distinguish correctly all fluorescent pixels inside and outside the vessels, and the segmentation result is used to evaluate the permeability of drug.