Skeletonization of 3D images using 2.5D and 3D algorithms

A 2D image is a rectangular array of what are called pixels and the pixel values represent intensity or color information of the corresponding digital image at various positions addressed by row and column numbers. A 2D image could also be informally called as image slice or a matrix of pixel values. A 3D image is generally viewed as an ordered sequence of 2D image slices arranged in the z-direction and the 3D arrangement of voxel values is called as a matroid of voxel values. Most of the 3D image processing operations are similar to those of 2D image processing. 3D images are processed with the help of 3D scanning windows, whereas 2D images are processed with the help of 2D scanning windows. For instance, a 3D image processing operation like 3D surface detection is carried out using analogous 2D edge detection algorithm on every image slice and the processed slices assembled to visualize 3D surface detected image. In fact, 2D contours of an image slice are called superficial features and closed surfaces of a 3D image are called volumetric features. One can always obtain surface detected version of a 3D image by processing the 2D image slices using 2D edge detection operation, and consequently the 3D surface detection operation is termed as 2.5D image processing. One could also process the 3D image data using a 3D surface detection algorithm, in which case it is termed as 3D image processing. This is not the case with the operation of skeeltonization. One cannot make use of 2.5D skeletonization operation of 2D image slices in order to get skeltonized version of the corresponding 3D image. This paper discusses the discrepancies and differences faced when 2.5D skeletonization of 2D image slices of a 3D image is carried out instead of the direct 3D skeletonization of the 3D image.