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hybrid method against all other studied methods. For the brain case, rigid registration is sufficient, but for other organs, non-rigid registration is required. For
that, we plan to test the hybrid method on other organs using diverse medical
imaging tools while comparing it with non-rigid registration tools.
References
1. Crum, W.R., Hartkens, T., Hill, D.L.G.: Non-rigid image registration: theory and
practice. Br. J. Radiol. 77(2), 140–153 (2004)
2. Baˆ azaoui, A., Berrabah, M., Barhoumi, W., Zagrouba, E.: 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.) ACIVS 2016.
LNCS, vol. 10016, pp. 361–372. Springer, Cham (2016). https://doi.org/10.1007/
978-3-319-48680-2 32
3. Barthel, H., Schroeter, M.L., Hoffmann, K.-T., Sabri, O.: PET/MR in dementia
and other neurodegenerative diseases. Semin. Nucl. Med. 45(3), 224–233 (2014)
4. Xu, Q., Hanna, G., Zhai, Y., Asbell, A., Fan, J.: Assessment of brain tumor displacements after skull based registration: a CT/MRI fusion study. Austin J. Radiat.
Oncol. Cancer 1, 1011 (2015)
5. Preuss, M., et al.: Integrated PET/MRI for planning navigated biopsies in pediatric
brain tumors. Child’s Nerv. Syst. 30(8), 1399–1403 (2014)
6. Schroeter, M.L., Neumann, J.: Combined imaging markers dissociate Alzheimer’s
disease and frontotemporal lobar degeneration–an ALE meta-analysis. Front.
Aging Neurosci. 3, 1–10 (2011)
7. Yushkevich, P.A., Gerig, G.: ITK-SNAP: an interactive medical image segmentation tool to meet the need for expert-guided segmentation of complex medical
images. IEEE Pulse 8(4), 54–57 (2017)
8. Xu, Z., et al.: Evaluation of six registration methods for the human abdomen on
clinically acquired CT. IEEE Trans. Biomed. Eng. 63(8), 1563–1572 (2016)
9. Penny, W.D., Friston, K.J., Ashburner, J.T., Kiebel, S.J., Nichols, T.E.: Statistical
Parametric Mapping: The Analysis of Functional Brain Images. Elsevier, London
(2011)
10. Kikinis, R., Pieper, S.D., Vosburgh, K.G.: 3D slicer: a platform for subject-specific
image analysis, visualization, and clinical support. In: Jolesz, F.A. (ed.) Intraoperative Imaging and Image-Guided Therapy, pp. 277–289. Springer, New York (2014).
https://doi.org/10.1007/978-1-4614-7657-3 19
11. Pieper, S., Lorensen, B., Schroeder, W., Kikinis, R.: The NA-MIC Kit: ITK, VTK,
pipelines, grids and 3D slicer as an open platform for the medical image computing community. In: International Symposium on Biomedical Imaging, pp. 698–701
(2006)
12. Xia, T., Qi, W., Niu, X., Asma, E., Winkler, M., Wang, W.: Quantitative comparison of anisotropic diffusion, non-local means and Gaussian post-filtering effects on
FDG-PET lesions. J. Nucl. Med. 56(3), 1797 (2015)
13. Dhahbi, S., Barhoumi, W., Zagrouba, E.: Breast cancer diagnosis in digitized mammograms using curvelet moments. Comput. Biol. Med. 64, 79–90 (2015). https://
doi.org/10.1016/j.compbiomed.2015.06.012
14. Rajwade, A., Banerjee, A., Rangarajan, A.: A new method of probability density
estimation with application to mutual information based image registration. In:
Conference on Computer Vision and Pattern Recognition, pp. 1769–1776 (2006)
M. Abderrahim et al.
hybrid method against all other studied methods. For the brain case, rigid registration is sufficient, but for other organs, non-rigid registration is required. For
that, we plan to test the hybrid method on other organs using diverse medical
imaging tools while comparing it with non-rigid registration tools.
References
1. Crum, W.R., Hartkens, T., Hill, D.L.G.: Non-rigid image registration: theory and
practice. Br. J. Radiol. 77(2), 140–153 (2004)
2. Baˆ azaoui, A., Berrabah, M., Barhoumi, W., Zagrouba, E.: 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.) ACIVS 2016.
LNCS, vol. 10016, pp. 361–372. Springer, Cham (2016). https://doi.org/10.1007/
978-3-319-48680-2 32
3. Barthel, H., Schroeter, M.L., Hoffmann, K.-T., Sabri, O.: PET/MR in dementia
and other neurodegenerative diseases. Semin. Nucl. Med. 45(3), 224–233 (2014)
4. Xu, Q., Hanna, G., Zhai, Y., Asbell, A., Fan, J.: Assessment of brain tumor displacements after skull based registration: a CT/MRI fusion study. Austin J. Radiat.
Oncol. Cancer 1, 1011 (2015)
5. Preuss, M., et al.: Integrated PET/MRI for planning navigated biopsies in pediatric
brain tumors. Child’s Nerv. Syst. 30(8), 1399–1403 (2014)
6. Schroeter, M.L., Neumann, J.: Combined imaging markers dissociate Alzheimer’s
disease and frontotemporal lobar degeneration–an ALE meta-analysis. Front.
Aging Neurosci. 3, 1–10 (2011)
7. Yushkevich, P.A., Gerig, G.: ITK-SNAP: an interactive medical image segmentation tool to meet the need for expert-guided segmentation of complex medical
images. IEEE Pulse 8(4), 54–57 (2017)
8. Xu, Z., et al.: Evaluation of six registration methods for the human abdomen on
clinically acquired CT. IEEE Trans. Biomed. Eng. 63(8), 1563–1572 (2016)
9. Penny, W.D., Friston, K.J., Ashburner, J.T., Kiebel, S.J., Nichols, T.E.: Statistical
Parametric Mapping: The Analysis of Functional Brain Images. Elsevier, London
(2011)
10. Kikinis, R., Pieper, S.D., Vosburgh, K.G.: 3D slicer: a platform for subject-specific
image analysis, visualization, and clinical support. In: Jolesz, F.A. (ed.) Intraoperative Imaging and Image-Guided Therapy, pp. 277–289. Springer, New York (2014).
https://doi.org/10.1007/978-1-4614-7657-3 19
11. Pieper, S., Lorensen, B., Schroeder, W., Kikinis, R.: The NA-MIC Kit: ITK, VTK,
pipelines, grids and 3D slicer as an open platform for the medical image computing community. In: International Symposium on Biomedical Imaging, pp. 698–701
(2006)
12. Xia, T., Qi, W., Niu, X., Asma, E., Winkler, M., Wang, W.: Quantitative comparison of anisotropic diffusion, non-local means and Gaussian post-filtering effects on
FDG-PET lesions. J. Nucl. Med. 56(3), 1797 (2015)
13. Dhahbi, S., Barhoumi, W., Zagrouba, E.: Breast cancer diagnosis in digitized mammograms using curvelet moments. Comput. Biol. Med. 64, 79–90 (2015). https://
doi.org/10.1016/j.compbiomed.2015.06.012
14. Rajwade, A., Banerjee, A., Rangarajan, A.: A new method of probability density
estimation with application to mutual information based image registration. In:
Conference on Computer Vision and Pattern Recognition, pp. 1769–1776 (2006)