Multimodal Registration of PET/MR Brain Images Based on Adaptive Mutual Information

Multimodal image registration remains a challenging task in medical image analysis, notably for PET/MR images since their combinations provide superior sensitivity and specificity, what improves the diagnosis quality. Mutual information (MI) is the commonly used multi-modal image registration measure. Inasmuch as the traditional MI, based on Shannon entropy, does not integrate the spatial information such as edges and corners, an adaptation of MI is proposed in this work. The two main contributions are the incorporation of the spatial information through the curvelet transform and the avoiding of the binning problem using Gaussian probability density function. The objective behind this adaptation is to ignore the sensitivity to intensity permutations or pixel-to-pixel intensity transformations and to simultaneously handle the positive and negative intensity correlations. Realized experiments on PET/MR image datasets demonstrated the effectiveness of the proposed method for PET/MR image registration and showed its superiority over state-of-the-art methods.