Abstract
Extending mutual information (MI), which has been widely used as a similarity measure for rigid registration of multi-modal images, to deformable registration is an active field of research. We propose a self-similarity weighted graph-based implementation of α-mutual information (α-MI) for nonrigid image registration. The new Self Similarity \(\underline \alpha\)-MI (SeSaMI) metric takes local structures into account and is robust against signal non-stationarity and intensity distortions. We have used SeSaMI as the similarity measure in a regularized cost function with B-spline deformation field. Since the gradient of SeSaMI can be derived analytically, the cost function can be efficiently optimized using stochastic gradient descent. We show that SeSaMI produces a robust and smooth cost function and outperforms the state of the art statistical based similarity metrics in simulation and using data from image-guided neurosurgery.
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Wells, W., Viola, P., Atsumid, H., Nakajimae, S., Kikinis, R.: Multi-modal volume registration maximization of mutual information. Med. Imag. Anal. 1, 35–51 (1996)
Maes, F., et al.: Multimodality image registration by maximization of mutual information. IEEE Trans. Medical Imag. 16, 187–198 (1997)
Pluim, J., Maintz, J., Viergever, M.: Mutual-information-based registration of medical images: a survey. IEEE Trans. Medical Imag. 22, 986–1004 (2003)
Studholme, C., Drapaca, C., Iordanova, B., Cardenas, V.: Deformation-based mapping of volume change from serial brain MRI in the presence of local tissue contrast change. IEEE Trans. Medical Imag. 25, 626–639 (2006)
Loeckx, D., Slagmolen, P., Maes, F., Vandermeulen, D., Suetens, P.: Nonrigid image registration using conditional mutual information. IEEE Trans. Medical Imag. 29, 19–29 (2010)
Klein, S., et al.: Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Med. Phys. 35, 1407–1417 (2008)
Zhuang, S., Arridge, D., Hawkes, D., Ourselin, S.: A nonrigid registration framework using spatially encoded mutual information and free-form deformations. IEEE Trans. Medical Imag. (in press)
Shechtman, E., Irani, M.: Matching local self-similarities across images and videos. In: Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2007)
Coupe, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., Barillot, C.: An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans. Med. Imag. 27, 425–441 (2008)
Heinrich, M.P., Jenkinson, M., Bhushan, M., Matin, T., Gleeson, F.V., Brady, J.M., Schnabel, J.A.: Non-local Shape Descriptor: A New Similarity Metric for Deformable Multi-modal Registration. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 541–548. Springer, Heidelberg (2011)
Staring, M., Heide, U., Klein, S., Pluim, J.: Registration of cervical MRI using multifeature mutual information. IEEE Trans. Medical Imag. 28, 1412–1421 (2009)
Sabuncu, M., Ramadge, P.: Using spanning graphs for efficient image registration. Information Processing Medical Imag. (IPMI) 17, 788–797 (2008)
Kybic, J., Vnucko, I.: Approximate all nearest neighbor search for high dimensional entropy estimation for image registration. Signal Processing 92, 1302–1316 (2012)
Neemuchwala, H., Hero, A.: Entropic graphs for registration. In: Blum, R., Liu, Z. (eds.) Multi-sensor Image Fusion and its Applications. CRC Press (2005)
Oubel, E., Craene, M., Hero, A., Frangi, A.: Cardiac motion estimation by joint alignment of tagged MRI sequences. Med. Imag. Anal. 16, 339–350 (2012)
Wein, W., et al.: Automatic ct-ultrasound registration for diagnostic imaging and image-guided intervention. Medical Imag. Analysis 12, 577–585 (2008)
Zhang, W., Brady, M., Becher, H., Noble, A.: Spatio-temporal (2d+t) non-rigid registration of real-time 3D echocardiography and cardiovascular MR image sequences. Physics Med. Biol. 56, 1341–1360 (2011)
Lazebnik, S., Schmid, C., Ponce, J.: A sparse texture representation using local affine regions. IEEE Trans. Pattern Anal. Machine Int. 27, 1265–1278 (2005)
Rubner, Y., Tomasi, C., Guibas, L.: The earth mover’s distance as a metric for image retrieval. IEEE Trans. Pattern Anal. Machine Int. 40, 99–121 (2000)
Klein, S., Staring, M., Pluim, J.: Evaluation of optimization methods for nonrigid medical image registration using mutual information and b-splines. IEEE Trans. Imag. Proc. 16, 2879–2890 (2007)
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Rivaz, H., Collins, D.L. (2012). Self-similarity Weighted Mutual Information: A New Nonrigid Image Registration Metric. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33454-2_12
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DOI: https://doi.org/10.1007/978-3-642-33454-2_12
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