Abstract
In this paper, we present a preliminary report of a multi-scale registration method between micro-focus X-ray CT (micro CT) volumes taken in different scales. 3D fine structures of target objects can be observed on micro CT volumes, which are difficult to observe on clinical CT volumes. Micro CT scanners can scan specimens in various resolutions. In their high resolution volumes, ultra fine structures of specimens can be observed, while scanned areas are limited to very small. On the other hand, in low resolution volumes, large areas can be captured, while fine structures of specimens are difficult to observe. The fusion volume of the high and low resolution volumes will have benefits of both. Because the difference of resolutions between the high and low resolution volumes may vary greatly, an intermediate resolution volume is required for successful fusion of volumes. To perform such volume fusion, a cascade multi-resolution registration technique is required. To register micro CT volumes that have quite different resolutions, we employ a cascade co-registration technique. In the cascade co-registration process, intermediate resolution volumes are used in a registration process of the high and low resolution volumes. In the registration between two volumes, we apply two steps registration techniques. In the first step, a block division is used to register two resolution volumes. Afterward, we estimate the fine spatial positions relating the registered two volumes using the Powell method. The registration result can be used to generate a fusion volume of the high and low resolution volumes.
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References
Boykov, Y., Jolly, M.-P.: Interactive organ segmentation using graph cuts. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 276–286. Springer, Heidelberg (2000)
Bracewell, R.: The Fourier Transform and Its Applications, 2nd edn. Mcgraw-Hill College, Pennsylvania (1986)
Campadelli, P., Pratissoli, S., Casiraghi, E., Lombardi, G.: Automatic abdominal organ segmentation from CT images. ELCVIA: Electron. Lett. Comput. Vis. Image Anal. 8(1), 1–14 (2009)
Heinrich, M.P., Jenkinson, M., Bhushan, M., Matin, T., Gleeson, F.V., Brady, S.M., Schnabel, J.A.: Mind: modality independent neighbourhood descriptor for multi-modal deformable registration. Med. Image Anal. 16(7), 1423–1435 (2012). Special Issue on the 2011 Conference on Medical Image Computing and Computer Assisted Intervention
Heitzman, E.R.: The Lung: Radiologic-Pathologic Correlations, 2nd edn. Mosby-Year Book, Maryland Heights (1984)
Kawata, Y., Niki, N., Umetani, K., Nakano, Y., Ohmatsu, H., Moriyama, N., Itoh, H.: Stochastic tracking of small pulmonary vessels in human lung alveolar walls using synchrotron radiation micro CT images. SPIE Med. Imaging 8672, 867211-1–867211-8 (2013)
Mitani, K., Saji, H.: Robust template matching by using variable size block division. In: Sumi, K. (ed.) Optomechatronic Technologies 2005, vol. 6051, pp. 60510X–60510X-8. International Society for Optics and Photonics, December 2005
Okada, T., Linguraru, M.G., Yoshida, Y., Hori, M., Summers, R.M., Chen, Y.-W., Tomiyama, N., Sato, Y.: Abdominal multi-organ segmentation of CT images based on hierarchical spatial modeling of organ interrelations. In: Sakas, G., Linguraru, M.G., Yoshida, H. (eds.) Abdominal Imaging. LNCS, vol. 7029, pp. 173–180. Springer, Heidelberg (2012)
Powell, M.J.D.: An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput. J. 7(2), 155–162 (1964)
Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)
Wachinger, C., Navab, N.: Entropy and Laplacian images: structural representations for multi-modal registration. Med. Image Anal. 16(1), 1–17 (2012)
Wan, S.Y., Kiraly, A.P., Ritman, E.L., Higgins, W.E.: Extraction of the hepatic vasculature in rats using 3-D micro-CT images. IEEE Trans. Med. Imaging 19(9), 964–971 (2000)
Watz, H., Breithecker, A., Rau, W.S., Kriete, A.: Micro-CT of the human lung: imaging of alveoli and virtual endoscopy of an alveolar duct in a normal lung and in a lung with centrilobular emphysema-initial observations. Radiology 236(3), 1053–1058 (2005)
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Parts of this research were supported by the MEXT, the JSPS KAKENHI Grant Numbers 25242047, 26108006, and the Kayamori Foundation of Informational Science Advancement.
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Nagara, K. et al. (2016). Cascade Registration of Micro CT Volumes Taken in Multiple Resolutions. In: Zheng, G., Liao, H., Jannin, P., Cattin, P., Lee, SL. (eds) Medical Imaging and Augmented Reality. MIAR 2016. Lecture Notes in Computer Science(), vol 9805. Springer, Cham. https://doi.org/10.1007/978-3-319-43775-0_24
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DOI: https://doi.org/10.1007/978-3-319-43775-0_24
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