Human cerebral image registration using generalized mutual information / 生物医学工程学杂志

ABSTRACT

Medical image registration is a highlight of actual research on medical image processing. Based onsimilarity measure of Shannon entropy, a new generalized distance measurement based on Rényi entropy applied to image rigid registration is introduced and is called here generalized mutual information (GMI). It is used in three dimensional cerebral image registration experiments. The simulation results show that generalized distance measurement and Shannon entropy measurement apply to different areas; that the registration measure based o n generalized distance is a natural extension of mutual information of Shannon entropy. The results prove that generalized mutual information uses less time than simple mutual information does, and the new similarity measure manifests higher degree of consistency between the two cerebral registration images. Also, the registration results provide the clinical diagnoses with more important references. In conclusion, generalized mutual information has satisfied the demands of clinical application to a wide extent.