A multicomponent approach to nonrigid registration of diffusion tensor images

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• 作者：Mohammed Khader ; Emanuele Schiavi ; A. Ben Hamza
• 关键词：Diffusion tensor imaging ; Image registration ; Nonrigid ; Tsallis entropy
• 刊名：Applied Intelligence
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：46
• 期：2
• 页码：241-253
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence (incl. Robotics); Mechanical Engineering; Manufacturing, Machines, Tools;
• 出版者：Springer US
• ISSN：1573-7497
• 卷排序：46

文摘

Diffusion tensor imaging has shown promise in the early detection and diagnosis of a host of disorders and neurologic conditions. In this paper, we propose a nonrigid registration approach for diffusion tensor images using a multicomponent information-theoretic measure. Explicit orientation optimization is enabled by incorporating tensor reorientation, which is necessary for wrapping diffusion tensor images. Experimental results on diffusion tensor images indicate the feasibility of the proposed approach and a much better performance compared to the affine registration method based on mutual information in terms of registration accuracy in the presence of geometric distortion.