Fuzzy object detection in 3D medical images

3D images are becoming more popular in the medical field. In order to extract desired objects with uncertainties in 3D images, we proposed a novel method to detect fuzzy boundaries with the aid of fuzzy connectivity relation derived from the theory of fuzzy digital topology. A seed point is interactively selected which is definitely inside the desired object, and is assigned an appropriate membership according to the statistic characteristics in its neighborhood, which is called "seed region'. Memberships of the other points in the image depend not only on their similarity of greylevel to the seed region, but also on their similarity to adjacent voxels. The later may be calculated from a point to the seed region on the basis of a newly defined concept of fuzzy connectivity. After such transformation, a fuzzy boundary is generated corresponding to a characteristic(seed) edge point that is selected by experiences or according to the requirement of practical applications. Furthermore, we may take use of skeleton extraction to optimize this boundary. Boundaries extracted like this are ensured to be Jordan. Experiments on some ultrasonic images show satisfying results with smoothed, connective and optimal boundaries.