The study of cerebral micro-vascular network requires high resolution images. However, to obtain statistically relevant results, a large area of the brain (about few square millimeters) has to be investigated. This leads us to consider huge images, too large to be loaded and processed at once in the memory of a standard computer. To consider a large area, a compact representation of the vessels is required. The medial axis seems to be the tools of choice for the aimed application. To extract it, a dedicated skeletonization algorithm is proposed. Indeed, a skeleton must be homotopic, thin and medial with respect to the object it represents. Numerous approaches already exist which focus on computational efficiency. However, they all implicitly assume that the image can be completely processed in the computer memory, which is not realistic with the size of the data considered here. We present in this paper a skeletonization algorithm that processes data locally (in sub-images) while preserving global properties (i.e. homotopy). We then show some results obtained on a mosaic of 3-D images acquired by confocal microscopy.
