Skip to main content
SpringerLink
Log in

Image Registration of Sectioned Brains

  • Published:
International Journal of Computer Vision Aims and scope Submit manuscript

Abstract

The physical (microtomy), optical (microscopy), and radiologic (tomography) sectioning of biological objects and their digitization lead to stacks of images. Due to the sectioning process and disturbances, movement of objects during imaging for example, adjacent images of the image stack are not optimally aligned to each other. Such mismatches have to be corrected automatically by suitable registration methods.

Here, a whole brain of a Sprague Dawley rat was serially sectioned and stained followed by digitizing the 20 μm thin histologic sections. We describe how to prepare the images for subsequent automatic intensity based registration. Different registration schemes are presented and their results compared to each other from an anatomical and mathematical perspective. In the first part we concentrate on rigid and affine linear methods and deal only with linear mismatches of the images. Digitized images of stained histologic sections often exhibit inhomogenities of the gray level distribution coming from staining and/or sectioning variations. Therefore, a method is developed that is robust with respect to inhomogenities and artifacts. Furthermore we combined this approach by minimizing a suitable distance measure for shear and rotation mismatches of foreground objects after applying the principal axes transform. As a consequence of our investigations, we must emphasize that the combination of a robust principal axes based registration in combination with optimizing translation, rotation and shearing errors gives rise to the best reconstruction results from the mathematical and anatomical view point.

Because the sectioning process introduces nonlinear deformations to the relative thin histologic sections as well, an elastic registration has to be applied to correct these deformations.

In the second part of the study a detailed description of the advances of an elastic registration after affine linear registration of the rat brain is given. We found quantitative evidence that affine linear registration is a suitable starting point for the alignment of histologic sections but elastic registration must be performed to improve significantly the registration result. A strategy is presented that enables to register elastically the affine linear preregistered~ rat brain sections and the first one hundred images of serial histologic sections through both occipital lobes of a human brain (6112 images). Additionally, we will describe how a parallel implementation of the elastic registration was realized. Finally, the computed force fields have been applied here for the first time to the morphometrized data of cells determined automatically by an image analytic framework.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Abbe, E. 1873. Beiträge zur Theorie des Mikroskops und der mikroskopischen Wahrnehmung. Arch. Mikr. Anat, 9:413.

    Google Scholar 

  • Abeles, M. 1991, Corticonics. Neural circuits of the cerebral cortex Cambridge University Press: Cambridge.

    Google Scholar 

  • Aferzon, J., Chau, R., and Cowan, D. 1991. A microcomputer-based system for three-dimensional reconstructions from tomographic or histologic sections. Anal. Quant. Cytol. Histol, 13:80–88.

    Google Scholar 

  • Alexander, M., Scarth, G., and Somorjai, R. 1997. An improved robust hierarchical registration algorithm. Magn. Reson. Imaging, 15:505–514.

    Article  Google Scholar 

  • Alpert, N., Bradshaw, J., Kennedy, D., and Correia, J. 1990. The principal axes transformation—a method for image registration. J. Nuc. Med, 31:1717–1722.

    Google Scholar 

  • Amit, Y., Grenander, U., and Piccioni, M. 1991. Structural image restoration through deformable templates. J. Am. Stat. Ass, 86:376–387.

    Article  Google Scholar 

  • Arbib, M. 1995, The Handbook of Brain Theory and Neural Networks MIT Press: Cambridge.

    Google Scholar 

  • Arsigny, V., Pennec, X., and Ayache, N. 2005. Polyrigid and polyaffine transformations: A novel geometrical tool to deal with non-rigid deformations—Application to the registration of histological slices. Med. Image. Anal, 9:507–523.

    Article  Google Scholar 

  • Ashburner, J., Andersson, J., and Friston, K. 2000. Image registration using a symmetric prior–in three dimensions. Hum. Brain. Mapp, 9:212–225.

    Article  Google Scholar 

  • Auer, M., Regitnig, P., and Holzapfel, G. 2005. An automatic nonrigid registration for stained histologic sections. IEEE Trans. Imag. Proc, 14:475–486.

    Article  Google Scholar 

  • Baheerathan, S., Albregtsen, F., and Danielsen, H. 1998. Registration of serial sections of mouse liver cell nuclei. J. Microsc, 192:37–53.

    Article  Google Scholar 

  • Bajcsy, R. 1982. Matching of deformed images. Proc. 6th Int. Conf. Patt. Recogn, 6:351–353.

  • Bajcsy, R. 1983. A computerized system for the elastic matching of deformed radiographic images to idealized atlas images. J. Comp. Ass. Tomo, 7:618–625.

    Article  Google Scholar 

  • Bajcsy, R. and Kovačíč, S. 1989. Multiresolution elastic matching. Comp. Vis. Image. Proc, 46:1–21.

    Article  Google Scholar 

  • Banerjee, P. and Toga, A. 1994. Image alignment by integrated rotational and translational transformation matrix. Phys. Med. Biol, 39:1969–1988.

    Article  Google Scholar 

  • Bardinet, E., Colchester, A., Roche, A., Zhu, Y., He, Y., Ourselin, S., Nailon, B., Hojjat, S., Ironside, J., Al-Sarraj, S., Ayache, N., and Wardlaw, J. 2001. Registration of reconstructed post mortem optical data with MR scans of the same patient. LNCS, 2208:957–965.

    Google Scholar 

  • Barnard, S. and Thompson, W. 1980. Disparity analysis of images. IEEE Trans. PAMI, 2:333–340.

    Google Scholar 

  • Barnea, D. and Silverman, H. 1972. A class of algorithms for fast digital image registration. IEEE Trans. Comp, 21:179–186.

    Article  MATH  Google Scholar 

  • Baumann, M. and Scharf, H. 1994. Moderne Bildverarbeitungsverfahren als Unterstützung der räumlichen Rekonstruktion histologischer Strukturen. Ann. Anat, 176:185–188.

    Google Scholar 

  • Benveniste, H. and Blackband, S. 2002. MR microscopy and high resolution small animal MRI: Applications in neuroscience research. Prog. Neurobiol, 67:393–420.

    Article  Google Scholar 

  • Böhme, M., Hagenau, R., Modersitzki, J., and Siebert, B. 2002. Non-linear image registration on PC-clusters using parallel FFT techniques. Technical Report SIIM-TR-A-02-08, Institute of Mathematics, Medical University of Lübeck.

  • Bookstein, F. 1984. A statistical method for biological shape comparisons. J. Theor. Biol, 107:475–520.

    Article  Google Scholar 

  • Bookstein, F. 1989. Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Trans. Patt. Anal. Mach. Intell, 11:567–585.

    Article  MATH  Google Scholar 

  • Borgefors, G. 1988. Hierarchical chamfer matching: A parametric edge matching algorithm. IEEE Trans. PAMI, 10:849–865.

    Google Scholar 

  • Born, G. 1883. Die Plattenmodellirungsmethode. Arch. Mikr. Anat, 22:584–599.

    Article  Google Scholar 

  • Braitenberg, V. 1978. Cell assemblies in the cerebral cortex. Lec. Notes. Biomath, 21:171–188.

    Google Scholar 

  • Bro-Nielsen, M. and Gramkow, C. 1996. Fast fluid registration of medical images. LNCS, 1131:267–276.

    Google Scholar 

  • Broit, C. 1981. Optimal registration of deformed images. Ph.D. thesis, Computer and Information science, University of Pensylvania.

  • Bron, C., Launay, D., Jourlin, M., Gautschi, H., Bächi, T., and Schüpbach, J. 1990. Three dimensional electron microscopy of entire cell. J. Mircosc, 157:115–126.

    Google Scholar 

  • Brown, L. 1992. A survey of image registration techniques. ACM Comp. Surv, 24:325–376.

    Article  Google Scholar 

  • Budo, A. 1990, Theoretische Mechanik VEB Deutscher Verlag der Wissenschaften.

  • Christensen, G. 1994. Deformable shape models for anatomy. Ph.D. thesis, Sever Institute of Technology, Washington University.

  • Christensen, G. and Johnson, H. 2001. Consistent image registration. IEEE Trans. Med. Imaging, 20:568–582.

    Article  Google Scholar 

  • Christensen, G., Joshi, S., and Miller, M. 1997. Volumetric transformation of brain anatomy. IEEE Trans. Med. Imaging, 16:864–877.

    Article  Google Scholar 

  • Chui, H., Win, L., Schultz, E., Duncan, J., and Rangarajan, A. 2001. A unified feature registration method for brain mapping. LNCS, 2082:300–314.

    Google Scholar 

  • Ciarlet, P. 2000. Mathematical Elasticity Elsevier Science.

  • Cohen, F., Yang, Z., Huang, Z., and Nissanov, J. 1998. Automatic matching of homologous histological sections. IEEE Trans. Biomed. Eng, 45:642–649.

    Article  Google Scholar 

  • Collins, D., Holmes, C., Peters, H., and Evans, A. 1995. Automatic 3D model-based neuroanatomical segmentation. Hum. Brain. Mapp, 3:190–208.

    Article  Google Scholar 

  • Dauguet, J., Mangin, J.-F., Delzescaux, T., and Frouin, V. 2004. Robust inter-slice intensity normalization using histogram scale-space analysis. LNCS, 3216:242–249.

    Google Scholar 

  • Davatzikos, C. 1997. Spatial transformation and registration of brain images using elastically deformable models. Comput. Vis. Image. Underst, 66:207–222.

    Article  Google Scholar 

  • Davatzikos, C. and Prince, J. 1994. Brain image registration based on curve mapping. Proc. IEEE Workshop. Biom. Image. Anal, 245–254.

  • de Castro, E. and Morandi, C. 1987. Registration of translated and rotated images using finite Fourier transforms. IEEE Trans. PAMI, 9:700–703.

    Google Scholar 

  • de Munck, J., Verster, F., Dubois, E., Habraken, J., Boltjes, B., Claus, J., and van Herk, M. 1998. Registration of MR and SPECT without using external fiducial markers. Phys. Med. Biol, 43:1255–1269.

    Article  Google Scholar 

  • Desgeorges, M., Derosier, C., Cordoliani, Y., Traina, M., de Soultrait, F., Bernard, C., Khadiri, M., and Debono, B. 1997. Imaging networks, surgical simulation, computer-assisted neurosurgery. J. Neuroradiol, 24:108–115.

    Google Scholar 

  • Dierker, M. 1976, An Algorithm for the Alignment of Serial Sections John Wiley & Sons: New York, P.B. Brown: Computer technology on neuroscience edition.

  • Dougherty, E. 1993, Mathematical Morphology in Image Processing Marcel Dekker: New York, Basel, Hong Kong.

    Google Scholar 

  • du Bois d’Aische, A., Craene, M. D., Geets, X., Gregoire, V., Macq, B., and Warfield, S. 2005. Efficient multi-modal dense field non-rigid registration: Alignment of histological and section images. Med. Image. Anal, 9:538–546.

    Article  Google Scholar 

  • Ferrant, M., Nabavi, A., Macq, B., Jolesz, F., Kikinis, R., and Warfield, S. 2001. Registration of 3-D intraoperative MR images of the brain using a finite-element biomechanical model. IEEE Trans. Med. Imaging, 20:1384–1397.

    Article  Google Scholar 

  • Fischer, A. and Modersitzki, J. 1999. Fast inversion of matrices arising in image processing. Num. Algo, 22:1–11.

    Article  MathSciNet  MATH  Google Scholar 

  • Fischer, A. and Modersitzki, J. 2001. A super fast registration algorithm. BVM, 22:168–173.

    Google Scholar 

  • Fischer, A. and Modersitzki, J. 2002. Fast diffusion registration. Contemp. Math, 313:117–129.

    MathSciNet  Google Scholar 

  • Fischer, M. and Elschlager, R. 1973. The representation and matching of pictorial structure. IEEE Trans. Comput, 1:67–92.

    Google Scholar 

  • Fortner. 1999. User’s Guide and Reference Manual Fortner Software: Boulder.

  • Fu, Y. and Ogden, R. 2001. Nonlinear Elasticity: Theory and Applications Cambridge University Press: Cambridge.

  • Gefen, S., Tretiak, O., and Nissanov, J. 2003. Elastic 3-D alignment of rat brain histological images. IEEE Trans. Med. Imag, 22:1480–1489.

    Article  Google Scholar 

  • Gerstein, G., Bedenbaugh, P., and Aertsen, A. 1989. Neuronal assemblies. IEEE Trans. Biomed. Engin, 36:4–14.

    Article  Google Scholar 

  • Glaser, J. and Glaser, M. 1965. A semi-automatic computer-microscope for the analysis of neuronal morphology. IEEE Trans. Biomed. Eng, 12:22–31.

    Google Scholar 

  • Gold, S., Rangarajan, A., Lu, C., Pappu, S., and Mjolsness, E. 1998. New algorithms for 2-D and 3-D point matching: pose estimation and correspondence. Pat. Recogn, 31:1019–1031.

    Article  Google Scholar 

  • Golub, G. and van Loan, C. 1989. Matrix Computations Second edition. The John Hopkins University Press: Baltimore.

  • Green, A. and Adkins, J. 1970. Large Elastic Deformations Clarendon Press: Oxford.

  • Green, A. and Zerna, W. 1968. Theoretical Elasticity Clarendon Press: Oxford.

  • Gremillet, P., Bron, C. Jourlin, M., Bachi, T., and Schüpbach, J. 1991. Dedicated image analysis techniques for three-dimensional reconstruction from serial sections in electron microscopy. Mach. Vis. Appl, 4:263–270.

    Article  Google Scholar 

  • Guimond, A., Roche, A., Ayache, N., and Meunier, J. 2001. Three-dimensional multimodal brain warping using the demons algorithm and adaptive intensity corrections. IEEE Trans. Med. Imaging, 20:58–69.

    Article  Google Scholar 

  • Hajnal, J., Saeed, N., Soar, E., Oatridge, A., Young, I., and Bydder, G. 1995. A registration and interpolation procedure for subvoxel matching of serially acquired MR images. J. Comput. Assist. Tomogr, 19:289–296.

    Article  Google Scholar 

  • Hamilton, P., McInerney, T., and Terzopoulos, D. 2001. Deformable organisms for automatic medical image analysis. LNCS, 2208:66–76.

    Google Scholar 

  • Hayakawa, N., Thevenaz, P., Nirkko, A., Uemura, M.U., Ishiwata, K., Shimada, Y., Ogi, N., Nagaoka, T., Toyama, H., Oda, K., Tanaka, A., Endo, K., and Senda, M. 2000. A PET-MRI registration technique for PET studies of the rat brain. Nucl. Med. Biol, 27:121–125.

    Article  Google Scholar 

  • Hebb, D. 1949, The Organization of Behavior Wiley: New York.

    Google Scholar 

  • Hellier, P., Barillot, C., Memin, E., and Perez, P. 2001. Hierarchical estimation of a dense deformation field for 3-D robust registration. IEEE Trans. Med. Imaging, 20:388–402.

    Article  Google Scholar 

  • Hibbard, L., Arnicar-Sulze, T., Dovey-Hartman, B., and Page, R. 1992. Computed alignment of dissimilar images for three-dimensional reconstructions. J. Neurosci. Methods, 41:133–152.

    Article  Google Scholar 

  • Hibbard, L., and Hawkins, R. 1988. Objective image alignment for three-dimensional reconstruction of digital autoradiograms. J Neurosci. Meth, 26:55–74.

    Article  Google Scholar 

  • Hibbard, L., McGlone, J., Davis, D., and Hawkins, R. 1987. Three-dimensional representation and analysis of brain energy metabolism. Science, 236:1641–1646.

    Article  Google Scholar 

  • Hill, D., Batchelor, P., Holden, M., and Hawkes, D. 2001. Medical image registration. Phys. Med. Biol, 46: R1–R45.

    Google Scholar 

  • Hoehn, M., Küstermann, E., Blunck, J., Wiedermann, D., Trapp, T., Wecker, S., Föking, M., Arnold, H., Hescheler, J., Fleischmann, B., Schwindt, W., and Bührle, C. 2002. Monitoring of implanted stem cell migration in vivo: a highly resolved in vivo magnetic resonance imaging investigation of experimental strocke in rat. Proc. Nat. Acad. Sci, 99:16267–16272.

    Article  Google Scholar 

  • Holden, M., Hill, D.H., Denton, E., Jarosz, J., Cox, T., Rohlfing, T., Goodey, J., and Hawkes, D. 2000. Voxel similarity measures for 3-D serial MR brain image registration. IEEE Trans. Med. Imaging, 19:94–102.

    Article  Google Scholar 

  • Horn, B. and Schunck, B. 1981. Determining optical flow. Art. Intell, 17:185–204.

    Article  Google Scholar 

  • Hsu, C., Wu, M., and Lee, C. 2001. Robust image registration for functional magnetic resonance imaging of the brain. Med. Biol. Eng. Comput, 39:517–524.

    Article  Google Scholar 

  • Hu, M. 1962. Visual pattern recognition by moment invariants. IEEE Trans. Inform. Theory, 8:179–187.

    Google Scholar 

  • Iosifescu, D., Fitzpatrick, J., Wang, M., Galloway, R.J., Maciunas, R., Allen, G., Shenton, M., Warfield, S., Kikinis, R., Dengler, J., Jolesz, F., and McCarley, R. 1997. An automated registration algorithm for measuring MRI subcortical brain structures. Neuroimage, 6:13–25.

    Article  Google Scholar 

  • Jacobs, M., Windham, J., Soltanian-Zadeh, H., Peck, D., and Knight, R. 1999. Registration and warping of magnetic resonance images to histological sections. Med. Phys, 26:1568–1578.

    Article  Google Scholar 

  • Jannin, P., Fleig, O., Seigneuret, E., Grova, C., Morandi, X., and Scarabin, J. 2000. A data fusion environment for multimodal and multi-informational neuronavigation. Comput. Aided. Surg, 5:1–10.

    Article  Google Scholar 

  • Johnson, E. and Capowski, J. 1983. A system for the three-dimensional reconstruction of biological structures. Comp. Biomed. Res, 16:79–87.

    Article  Google Scholar 

  • Johnson, H. and Christensen, G. 2001. Landmark and intensity-based, consistent thin-plate spline image registration. LNCS, 2082:329–343.

    Google Scholar 

  • Joshi, S. and Miller, M. 2000. Landmark matching via large deformation diffeomorphisms. IEEE Trans. Image. Proc, 9:1357–1370.

    Article  MathSciNet  MATH  Google Scholar 

  • Juan, M., Alcaniz, B., Hernandez, V., Montesinos, A., Barcia, J., Monserrat, C., and Grau, V. 2000. A new efficient method for 3D registration using human brain atlases. Stud. Health. Technol. Inform, 70:153–155.

    Google Scholar 

  • Kent, J. and Tyler, D. 1988. Maximum likelihood estimation for the wrapped Cauchy distribution. J. Appl. Stat, 15:247–254.

    Google Scholar 

  • Kiebel, S., Ashburner, J., Poline, J., and Friston, K. 1997. MRI and PET coregistration–a cross validation of statistical parametric mapping and automated image registration. Neuroimage, 5:271–279.

    Article  Google Scholar 

  • Kosevich, A. 1995, Theory of Elasticity 3rd Ed. Butterworth Heinemann, Oxford.

  • Kostelec, P., Weaver, J., and Healy, D. J. 1998. Multiresolution elastic image registration. Med. Phys, 25:1593–1604.

    Article  Google Scholar 

  • Kremser, C., Plangger, C., Boesecke, R., Pallua, A., Aichner, F., and Felber, S. 1997. Image registration of MR and CT images using a frameless fiducial marker system. Mag. Res. Imag, 15:579–585.

    Article  Google Scholar 

  • Kuglin, C. and Hines, D. 1975. The phase correlation image alignment method. Proc. IEEE Int. Conf. Cyb. Soc, 163–165.

  • Kuljis, R. and Rakic, P. 1990. Hypercolumns in primate visual cortex can develop in the absence of cues from photoreceptors. Proc. Natl. Acad. Sci. USA, 87:5303–5306.

    Article  Google Scholar 

  • Kullback, S. and Leibler, R. 1951. On information and sufficiency. Ann. Math. Statist, 122:79–86.

    MathSciNet  Google Scholar 

  • Lamadø, W., Glombitza, G., Demiris, A., Cardenas, C., Thorn, M., Meinzer, H., Grenacher, L., Bauer, H., Lehnert, T., and Herfarth, C. 2000. The impact of 3-dimensional reconstructions on operation planing in liver surgery. Arch. Surg, 135:1256–1261.

    Article  Google Scholar 

  • Lester, H. and Arridge, S. 1999. A survey of hierarchical non-linear medical image registration. Pat. Rec, 32:129–149.

    Article  Google Scholar 

  • Likar, B. and Pernus, F. 1999. Automatic extraction of corresponding points for the registration of medical images. Med. Phys, 26:1678–1686.

    Article  Google Scholar 

  • Lurie, A. 1990, Nonlinear Theory of Elasticity North-Holland: Amsterdam.

    MATH  Google Scholar 

  • Macagno, E., Levinthal, C., and Sobel, I. 1979. Three-dimensional computer reconstruction of neurons and neuronal assemblies. Annu. Rev. Biophys. Bioeng, 8:323–351.

    Article  Google Scholar 

  • Macagno, E., Levinthal, C., Tountas, C., Bornholdt, R., and Abba, R. 1976, Recording and Analysis of 3-D Information from Serial Section Micrographs: The Cartos System Hemisphere Publishing Corporation: Washington, P.B. Brown: Computer technology in neuroscience edition.

  • MacDonald, D., Kabani, N., Avis, D., and Evans, A. 2000. Automated 3d extraction of inner and outer surfaces of cerebral cortex from MRI. NeuroImage, 12:340–356.

    Article  Google Scholar 

  • Maintz, J. and Viergever, M. 1981. A survey of medical image registration. Med. Image. Anal, 2:1–36.

    Article  Google Scholar 

  • Malandain, G. and Bardinet, E. 2003. Intensity compensation within series of images. LNCS, 2879:41–49.

    Google Scholar 

  • Malandain, G., Bardinet, E., Nelissen, K., and Vanduffel, W. 2004. Fusion of autoradiographs with an MR volume using 2-D and 3-D linear transformations. NeuroImage, 23:111–127.

    Article  Google Scholar 

  • Maurer, C. and Fitzpatrick, J. 1993. Interactive Image-Guided Neurosurgery R.J. Maciunas (Ed.). A review of medical image registration, American Association of Neurological Surgeons, Park Ridge, IL, pp. 17–44.

  • Maurer, C., Fitzpatrick, J., Wang, M., Galloway, R., Maciunas, R., and GS, G.A. 1997. Registration of head volume images using implantable fiducial markers. IEEE Trans. Med. Imag, 16:447–462.

    Article  Google Scholar 

  • Maurer, C., Hill, D., Martin, A., Liu, H., McCue, M., Rueckert, D., Lloret, D., Hall, W., Maxwell, R., Hawkes, D., and Truwit, C. 1998a. Investigation of intraoperative brain deformation using a 1.5-T interventional MR system: preliminary results. J. Anat, 193:347–361.

    Article  Google Scholar 

  • Maurer, C., Hill, D., Martin, A., Liu, H., McCue, M., Rueckert, D., Lloret, D., Hall, W., Maxwell, R., Hawkes, D., and Truwit, C. 1998b. Investigation of intraoperative brain deformation using a 1.5-T interventional MR system: preliminary results. IEEE Trans. Med. Imaging, 17:817–825.

    Article  Google Scholar 

  • Maurer, C., Maciunas, R., and Fitzpatrick, J. 1998c. Registration of head CT images to physical space using a weighted combination of points and surfaces. IEEE Trans. Med. Imaging, 17:753–761.

    Article  Google Scholar 

  • McInerney, J. and Roberts, D. 1998. An object-based volumetric deformable atlas for the improved localization of neuroanatomy in MR images. LNCS, 1496:861–869.

    Google Scholar 

  • Mega, M., Berdichevsky, D., Levin, Z., Morris, E., Fischman, A., Chen, S., Thompson, P., Woods, R., Karaca, T., Tiwari, A., Vinters, H., Small, G., and Toga, A. 1997. Mapping histology to metabolism: Coregistration of stained whole-brain sections to premortem PET in Alzheimer’s disease. Neuroimage, 5:147–153.

    Article  Google Scholar 

  • Miller, K. and Chinzei, K. 1997. Constitutive modelling of brain tissue: experiment and theory. J. Biomech, 30:1115–1121.

    Article  Google Scholar 

  • Modersitzki, J. 2004, Numerical Methods for Image Registration Oxford University Press.

  • Modersitzki, J., Obelöer, W., Schmitt, O., and Lustig, G. 1999. Elastic matching of very large digital images on high performance clusters. LNCS, 1593:141–149.

    Google Scholar 

  • Mountcastle, V. 1997. The columnar organization of the neocortex. Brain, 120:701–722.

    Article  Google Scholar 

  • Murphy, M., O’Brien, T., Morris, K., and Cook, M. 2001. Multimodality image-guided epilepsy surgery. J. Clin. Neurosci, 8:534–538.

    Article  Google Scholar 

  • Mutic, S., Hellier, P., Barillot, C., Dempsey, J., Bosch, W., Low, D., Drzymala, R., Chao, K., Goddu, S., Cutler, P., and Purdy, J. 2001. Multimodality image registration quality assurance for conformal three-dimensional treatment planning. Int. J. Radiat. Oncol. Biol. Phys, 51:255–260.

    Article  Google Scholar 

  • Nowinski, W., Scarth, G., Somorjai, R., Fang, A., Nguyen, B., Raphel, J., Jagannathan, L., Raghavan, R., Bryan, R., and Miller, G. 1997. Multiple brain atlas database and atlas-based neuroimaging system. Comput. Aided. Surg, 2:42–66.

    Article  Google Scholar 

  • Nowinski, W. and Thirunavuukarasuu, A. 2001. Atlas-assisted localization analysis of functional images. Med. Image. Anal, 5:207–220.

    Article  Google Scholar 

  • Okajima, K. 1986. A mathematical model of the primary cortex and hypercolumn. Biol. Cyber, 54:107–114.

    Article  MATH  Google Scholar 

  • Ongaro, I., Sperber, G., Machin, G., and Murdoch, C. 1991. Fiducial points for three-dimensional computer-assisted reconstruction of serial light microscopic sections of umbilical cord. Anat. Rec, 229:285–289.

    Article  Google Scholar 

  • Otte, M. 2001. Elastic registration of fMRI data using Bezier-spline transformations. IEEE Trans. Med. Imaging, 20:193–206.

    Article  Google Scholar 

  • Ourselin, S., Bardinet, E., Dormont, D., Malandain, G., Roche, A., Ayache, N., Tandé, D. Parain, K., and Yelnik, J. 2001a. Fusion of histological sections and MR images: towards the construction of an atlas of the human basal ganglia. LNCS, 2208:743–751.

    Google Scholar 

  • Ourselin, S., Roche, A., Subsol, G., Pennec, X., and Ayache, N. 2001b. Reconstructing a 3D structure from serial histologic sections. Image. Vis. Comp, 19:25–31.

    Article  Google Scholar 

  • Ozturk, C. 2002. Align1.1. http://www.neuroterrain.org/~webbyproduction/body/index.html.

  • Palm, G. 1982, Studies of Brain Function: Neural Assemblies Springer: Berlin.

    Google Scholar 

  • Pawley, J. 1995, Handbook of Biological Confocal Microscopy Plenum: New York.

    Google Scholar 

  • Penney, G., Weese, J., Little, J., Desmedt, P., Hill, D., and Hawkes, D. 1998. A comparison of similarity measures for use in 2-D-3-D medical image registration. IEEE Trans. Med. Imaging, 17:586–595.

    Article  Google Scholar 

  • Perkins, W. and Green, R. 1982. Three-dimensional reconstruction of biological sections. J. Biomed. Eng 4:37–43.

    Article  Google Scholar 

  • Rangarajan, A., Chui, H., and Duncan, J. 1999. Rigid point feature registration using mutual information. Med. Image. Anal, 3:425–440.

    Article  Google Scholar 

  • Rangarajan, A., Chui, H., Mjolsness, E., Pappu, S., Davachi, L., Goldman-Rakic, P., and Duncan, J. 1997. A robust point matching algorithm for autoradiographic alignment. Med. Image. Anal, 4:379–398.

    Article  Google Scholar 

  • Rohlfing, T. and Maurer, C. 2001. Intensity-based non-rigid registration using adaptive multilevel free-form deformation with an incompressibility constraint. LNCS, 2208:111–119.

    Google Scholar 

  • Rohlfing, T., West, J., Beier, J., Liebig, T., Taschner, C., and Thomale, U. 2000. Registration of functional and anatomical MRI: accuracy assessment and application in navigated neurosurgery. Comput. Aided. Surg, 5:414–425.

    Article  Google Scholar 

  • Rohr, K., Stiehl, H., Sprengel, R., Buzug, T., Weese, J., and Kuhn, M. 2001. Landmark-based elastic registration using approximating thin-plate splines. IEEE Trans. Med. Imaging, 20:526–534.

    Article  Google Scholar 

  • Rouet, J., Jacq, J., and Roux, C. 2000. Genetic algorithms for a robust 3-D MR-CT registration. IEEE Trans. Inf. Technol. Biomed, 4:126–136.

    Article  Google Scholar 

  • Rueckert, D., Sonoda, L., Hayes, C., Hill, D., Leach, M., and Hawkes, D. 1999. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging, 18:712–721.

    Article  Google Scholar 

  • Rusinek, H., Tsui, W.-H., Levy, A., Noz, M., and de Leon, M. 1993. Principal axes and surface fitting methods for three-dimensional image registration. J. Nuc. Med, 34:2019–2024.

    Google Scholar 

  • Russo, R. 1996, Mathematical Problems in Elasticity World Scientific Publ: Singapore.

    MATH  Google Scholar 

  • Sabbah, P., Zagrodsky, V., Foehrenbach, H., Dutertre, G., Nioche, C., DeDreuille, O., Bellegou, N., Mangin, J., Leveque, C., Faillot, T., Gaillard, J., Desgeorges, M., and Cordoliani, Y. 2002. Multimodal anatomic, functional, and metabolic brain imaging for tumor resection. Clin. Imaging, 26:6–12.

    Article  Google Scholar 

  • Santori, E. and Toga, A. 1993. Superpositioning of 3-dimensional neuroanatomic data sets. J. Neurosci. Methods, 50:187–196.

    Article  Google Scholar 

  • Schieweck, F. 1993. A parallel multigrid algorithm for solving the Navier-Stockes equations. Imp. Comp. Sci. Eng, 5:345–378.

    Article  MathSciNet  MATH  Google Scholar 

  • Schlaug, G., Schleicher, A., and Zilles, K. 1995. Quantitative analysis of the columnar arrangement of neurons in the human cingulate cortex. J. Comp. Neurol, 351:441–452.

    Article  Google Scholar 

  • Schmitt, O. and Eggers, R. 1997a. High contrast and homogeneous staining of paraffin sections of whole human brains for three dimensional ultrahigh resolution image analysis. Biotech. Histochem, 73:44–51.

    Article  Google Scholar 

  • Schmitt, O. and Eggers, R. 1997b. Systematic investigations of the contrast results of histochemical stainings of neurons and glial cells in the human brain by means of image analysis. Micron, 28:197–215.

    Article  Google Scholar 

  • Schmitt, O. and Eggers, R. 1999. Flat-bed scanning as a tool for quantitative neuroimaging. J. Microsc, 196:337–346.

    Article  Google Scholar 

  • Schmitt, O., Eggers, R., and Modersitzki, J. 2005. Videomicroscopy, image processing, and analysis of whole histologic sections of the human brain. Micr. Res. Tech, 66:203–218.

    Article  Google Scholar 

  • Schmitt, O., Modersitzki, J., and Obelöer, W. 1999. The human neuroscanning project. Neuroimage, 9:S22.

    Google Scholar 

  • Schmolke, C. 1996. Tissue compartments in laminae II-V of rabbit visual cortex–three-dimensional arrangement, size and developmental changes. Anat. Embryol, 193:15–33.

    Article  Google Scholar 

  • Schmolke, C. and Fleischhauer, K. 1984. Morphological characteristics of neocortical laminae when studied in tangential semithin sections through the visual cortex of the rabbit. Anat. Embryol, 169:125–133.

    Article  Google Scholar 

  • Schormann, T. 1996. A new approach to fast elastic alignment with applications to human brains. LNCS, 1131:337–342.

    Google Scholar 

  • Schormann, T., Darbinghaus, A., and Zilles, K. 1997. Extension of the principle axes theory for the determination of affine transformations. Informatik aktuell, 19:384–391.

    Google Scholar 

  • Schormann, T. and Zilles, K. 1997. Limitations of the principal axes theory. IEEE Trans. Med. Imag, 16:942–947.

    Article  Google Scholar 

  • Schormann, T. and Zilles, K. 1998. Three-dimensional linear and nonlinear transformations: an integration of light microscopical and MRI data. Hum. Brain. Mapp, 6:339–347.

    Article  Google Scholar 

  • Silva, A. and Koretsky, A. 2002. Laminar specificity of functional MRI onset times during somatosensory stimulation in rat. Proc. Nat. Acad. Sci, 99:15182–15187.

    Article  Google Scholar 

  • Sjöstrand, R. 1958. Ultrastructure of retinal rod synapses of the guinea pig eye as revealed by 3-D reconstructions from serial sections. J. Ultrastruct. Res, 2:122–170.

    Article  Google Scholar 

  • Sokolnikoff, I. 1956, Mathematical Theory of Elasticity McGraw-Hill: New York.

    MATH  Google Scholar 

  • Street, C. and Mize, R. 1983. A simple microcomputer-based three-dimensional serial reconstruction system (MICROS). J. Neurosci. Meth, 7:359–375.

    Article  Google Scholar 

  • Studholme, C., Hill, D., and Hawkes, D. 1999. An overlap invariant entropy measure of 3D medical image alignment. Pat. Recog, 32:71–86.

    Article  Google Scholar 

  • Symon, K. 1971, Mechanics 3rd edition, Addison-Wesley: Reading, MA.

    Google Scholar 

  • Tanaka, S. 1991. Theory of ocular dominance column formation. Biol. Cyber, 64:263–272.

    Article  Google Scholar 

  • Thirion, J.-P. 1998. Image matching as a diffusion process: An analogy with Maxwell’s demons. Med. Image. Anal, 2:243–260.

    Article  Google Scholar 

  • Thompson, J., Peterson, M., and Freeman, R. 2003. Single-Neuron activity and tissue oxygenation in the cerebral cortex. Science, 299:1070–1072.

    Article  Google Scholar 

  • Thompson, M. and Ferziger, J. 1989. An adaptive multigrid technique for the incompressible Navier-Stockes equations. J. Comp. Phys, 82:94–121.

    Article  MATH  Google Scholar 

  • Thurfjell, L., Bohm, C., and Bengtsson, E. 1995. CBA–an atlas-based software tool used to facilitate the interpretation of neuroimaging data. Comput. Methods. Programs. Biomed, 47:51–71.

    Article  Google Scholar 

  • Toga, A. and Banerjee, P. 1993. Registration revisited. J. Neurosci. Meth, 48:1–13.

    Article  Google Scholar 

  • Toga, A., Santori, E., Hazani, R., and Ambach, K. 1995. A 3D digital map of rat brain. Brain. Res. Bull, 38:77–85.

    Article  Google Scholar 

  • Toga, A. and Thompson, P. 2001. The role of image registration in brain mapping. Image. Vis. Comp, 19:3–24.

    Article  Google Scholar 

  • van den Elsen, P., Pol, E.-J., and Viergever, M. 1993. Medical image matching - a review with classification. IEEE Eng. Med. Biol, 12:26–39.

    Article  Google Scholar 

  • van Essen, D. 1997. A tension-based theory of morphologenesis and compact wiring in the central nervous system. Nature, 285:313–318.

    Article  Google Scholar 

  • Vatsa, V. and Wedan, B. 1990. Development of a multigrid code for 3-D Navier-Stokes equations and its application to a grid-refinement study. Comp. Fluids, 18:391–403.

    Article  MATH  Google Scholar 

  • Viergever, M., Maintz, J., and Stokking, R. 1997. Integration of functional and anatomical brain images. Biophys. Chem, 68:207–219.

    Article  Google Scholar 

  • Viola, P. and Wells, W. 1993. Alignment by maximization of mutual information—a review with classification. 5th Int. Conf. Comp. Vis., IEEE, 5:16–23.

  • Viola, P. and Wells, W.: 1997. Alignment by maximization of mutual information. Int. J. Comp. Vision, 24:137–154.

    Article  Google Scholar 

  • Ware, R. and LoPresti, V. 1975. Three-Dimensional reconstruction from serial sections. Int. Rev. Cytol, 40:325–440.

    Article  Google Scholar 

  • Watanabe, H., Andersen, F., Simonsen, C., Evans, S., Gjedde, A., and Cumming, P. 2001. MR-based statistical atlas of the Gottingen minipig brain. Neuroimage, 14:1089–1096.

    Article  Google Scholar 

  • Webster, R. 1994. An algebraic multigrid solver for Navier-Stokes problems. Int. J. Num. Meth. Fluids, 18:761–780.

    Article  MATH  Google Scholar 

  • West, J., Fitzpatrick, J., Toms, S., Maurer, C., and Maciunas, R. 2001. Fiducial point placement and the accuracy of point-based, rigid body registration. Neurosurgery, 48:810–817.

    Article  Google Scholar 

  • White, E. 1989, Cortical Circuits. Synaptic Organization of the Cerebral Cortex. Structure, Function, and Theory Birkhäuser, Boston.

  • Widrow, B. 1973. The rubber-mask technique. I. Pattern Measurement and analysis. Pat. Recog, 5:175–197.

    Article  Google Scholar 

  • Woods, R. 2002. AIR 5.08. http://bishopw.loni.ucla.edu/AIR5/index.html.

  • Woods, R., Dapretto, M., Sicotte, N., Toga, A., and Mazziotta, J. 1999. Creation and use of a Talairach-compatible atlas for accurate, automated, nonlinear intersubject registration, and analysis of functional imaging data. Hum. Brain. Mapp, 8:73–79.

    Article  Google Scholar 

  • Woods, R., Grafton, S., Holmes, C., Cherry, S., and Mazziotta, J. 1998a. Automated image registration: I. General methods and intrasubject, intramodality validation. J. Comput. Assist. Tomogr, 22:139–152.

    Article  Google Scholar 

  • Woods, R., Grafton, S., Watson, J., Sicotte, N., and Mazziotta, J. 1998b. Automated image registration: II. Intersubject validation of linear and nonlinear models. J. Comput. Assist. Tomogr, 22:153–165.

    Article  Google Scholar 

  • Yeshurun, Y. and Schwartz, E. 1999. Cortical hypercolumn size determines stereo fusion limits. Biol. Cyber, 80:117–129.

    Article  MATH  Google Scholar 

  • You, J. 1995. Efficient image matching: A hierarchical chamfer matching scheme via distributed system. Real-time Imag, 1:245–259.

    Article  Google Scholar 

  • Young, M. 1992. Objective analysis of the topological organization of the primate cortical visual system. Nature, 358:152–155.

    Article  Google Scholar 

  • Young, M. 1996, The Analysis of Cortical Connectivity Springer.

  • Zeiss: 1992. KS400 Reference Guide Zeiss Vision: Jena.

  • Zhao, W., Young, T., and Ginsberg, M. 1993. Registration and three-dimensional reconstruction of autoradiographic images by the disparity analysis method. IEEE Trans. Med. Imag, 12:782–791.

    Article  Google Scholar 

  • Zhu, Y. 2002. Volume image registration by cross-entropy optimization. IEEE Trans. Med. Imaging, 21:174–180.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Anatomy, University of Rostock, Gertrudenstr. 9, D-18055, Rostock, Germany

    Oliver Schmitt

  2. Institute of Mathematics, University of Lübeck, Wallstraße 40, D-23560, Lübeck, Germany

    Jan Modersitzki, Stefan Heldmann & Stefan Wirtz

  3. Institute of Mathematics, Medical University of Lübeck, Wallstraße 40, D-23560, Lübeck, Germany

    Bernd Fischer

Authors
  1. Oliver Schmitt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jan Modersitzki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefan Heldmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Stefan Wirtz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bernd Fischer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oliver Schmitt.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Schmitt, O., Modersitzki, J., Heldmann, S. et al. Image Registration of Sectioned Brains. Int J Comput Vision 73, 5–39 (2007). https://doi.org/10.1007/s11263-006-9780-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11263-006-9780-x

Keywords

Search

Navigation