Normalized entropy measure for multimodality image alignment

Colin Studholme; David John Hawkes; Derek L.G. Hill

doi:10.1117/12.310835

Translator Disclaimer

24 June 1998 Normalized entropy measure for multimodality image alignment

Colin Studholme, David John Hawkes, Derek L.G. Hill

Author Affiliations +

Proceedings Volume 3338, Medical Imaging 1998: Image Processing; (1998) https://doi.org/10.1117/12.310835
Event: Medical Imaging '98, 1998, San Diego, CA, United States

Abstract

Automated multi-modality 3D medical image alignment has been an active area of research for many years. There have been a number of recent papers proposing and investigating the use of entropy derived measures of brain image alignment. Any registration measure must allow us to choose between transformation estimates based on the similarity of images within their volume of overlap. Since 3D medical images often have a limited extent and overlap, the similarity measure for the two transformation estimates may be derived from two very different regions within the images. Direct measures of information such as the joint entropy and mutual information will therefore be a function of, not only image similarity in the region of overlap, but also of the local image content within the overlap. In this paper we present a new measure, normalized mutual information, which is simply the ratio of the sum of the marginal entropies and the joint entropy. The effect of changing overlap on current entropy measures and this normalized measure are compared using a simple image model and experiments on clinical MR-PET and MR-CT image data. Results indicate that the normalized entropy measure provides significantly improved behavior over a range of imaged fields of view.

Citation Download Citation

Colin Studholme, David John Hawkes, and Derek L.G. Hill "Normalized entropy measure for multimodality image alignment", Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); https://doi.org/10.1117/12.310835

ACCESS THE FULL ARTICLE