Autonoma de Barcelona, Bellaterra, Spain. This paper describes a method which uses the skull as a landmark for automatic registration of CT to MR images. First, the skull is extracted from both images using a new creaseness operator. Then, the resulting creaseness images are used to build a hierarchic structure which permits a robust and fast search. We have justified experimentally the performance of several choices of our algorithm, and we have thoroughly tested its accuracy and roughness against the well-known mutual information method for 5 different pairs of images.
Most verbal communications use cues from both the visual and acoustic modalities to convey messages.
During the production of speech, the visible information provided by the external articulatory organs can influence the understanding of the language, by interpreting the combined information into meaningful linguistic expressions. The task of integrating speech and image data to emulate the bimodal human interaction system c an be depicted by developing automated systems. These systems have a wide range of applications such as the videophone systems, where the interdependencies between image and speech signals can be exploited for data compression and in solving the task of lip synchronization which has been a major problem. Abstract Nonlinear diffusion processes and watershed algorithms have been well studied for gray-scale image segmentation. In this paper we extend the use of these techniques to color or multichannel images.