Deformable multi-modal registration using 3D-FAST conditioned mutual information

Abstract

Purpose: Mutual information (MI) has been a preferred choice of similarity measure for multi-modal image registration, but the accuracy and robustness of MI are not satisfactory as MI only considers the global intensity correlation while ignoring local and structural information. To address this problem, we combine MI with local and structural information.Method: We bring structural information extracted by a modified Accelerated Segment Test (FAST) algorithm into MI. Traditional FAST is transferred into 3 D for the first time, and the 3 D-FAST based structural information is added into MI as another channel, thereby incorporating spatial and geometric information with intensity information in the registration.Result: The robustness and accuracy of the proposed method were demonstrated in three experiments. The average registration errors of our method were 1.17, 1.33 and 1.20 compared to 1.47, 1.63 and 1.40 of LMI in T1-T2, T1-PD and T2-PD registration respectively.Discussion: In this paper, we use the structural similarity computed by 3 D-FAST as the conditional information to encode spatial and geometric cues into LMI. In all of these three experiments, our method shows to be more robust and accurate than common registration methods based on information theory.