Abstract
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 multimodal 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.
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Acknowledgement
The authors would like to thank Dr. Rostom Mabrouk, Centre for Addiction and Mental Health (CAMH), University of Toronto, Toronto, Canada; who generously allowed us to use their set of PET/MRI benchmark test for our experimental study. The authors would like to thank also Dr. Md. Mushfiqul Alam, School of Electrical and Computer Engineering, Oklahoma State, Stillwater, USA; for providing us the permission to use their released comparison results.
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Baâzaoui, A., Berrabah, M., Barhoumi, W., Zagrouba, E. (2016). Multimodal Registration of PET/MR Brain Images Based on Adaptive Mutual Information. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Lecture Notes in Computer Science(), vol 10016. Springer, Cham. https://doi.org/10.1007/978-3-319-48680-2_32
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DOI: https://doi.org/10.1007/978-3-319-48680-2_32
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