Full-Text Search:
Home|Journal Papers|About CNKI|User Service|FAQ|Contact Us|中文
《Optical Technique》 2005-05
Add to Favorite Get Latest Update

Novel divergence measure based on Cauchy-Schwarz for multimodal medical image registration

SHI Yong-gang(School of Information Technology, Beijing Institute of Technology, Beijing 100081, China)  
Information theoretic similarity measures, especially mutual information, have been widely and successfully employed in multimodal image registration. Apart from these metrics, however, there are other measures which could be considered for image registration. A new similarity metrics was introduced for this task. The connections between Shannon mutual information, Kullback-Leibler divergence and Shannon inequality were analyzed. From these connections and Cauchy-Schwarz inequality, a novel generalized divergence was proposed. According to the new divergence function, a novel similarity measure based on Cauchy-Schwarz inequality for multi-modal image registration was put forward. Unlike Kullback-Leibler divergence, the new measures have some fundamental and appealing mathematical properties such as convexity, symmetry and do not require the condition of absolute continuity to be satisfied by the probability distribution involved. The performance of mutual information and normalized mutual information with the new similarity measures were compared. These measures are applied to rigid registration of clinical MR/PET images. An accurate gold standard transformation is avail able for the images. The results of tests indicated these new similarity functions have even better performance, compared with Shannon information theoretic measures.
【Fund】: 国家自然科学基金资助项目(60402037);; 北京理工大学信息科学青年基金资助项目
【CateGory Index】: TP399
Download(CAJ format) Download(PDF format)
CAJViewer7.0 supports all the CNKI file formats; AdobeReader only supports the PDF format.
©2006 Tsinghua Tongfang Knowledge Network Technology Co., Ltd.(Beijing)(TTKN) All rights reserved