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Three‐Dimensional Image Visualization and Analysis

Stephen J. Lockett¹

¹Lawrence Berkeley National Laboratory, Berkeley, California

Publication Name:

Current Protocols in Cytometry

Unit Number:

Unit 10.10

DOI:

10.1002/0471142956.cy1010s10

Online Posting Date:

May, 2001

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Abstract

This unit introduces the concepts of 3D image analysis and visualization as applied in cytometry. The author discusses the nature of 3D data sets and describes the techniques for visualization and analysis of 3D images. Discussions of noise removal, depth attenuation, and correction and segmentation are also included, as is a brief introduction to 3D analysis options and deconvolution prinicples. This commentary unit is a good way to begin an understanding of the application of 3D data sets.

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Unit Introduction
Definitions
Description of a 3D Image
Display of 3D Images
Volume Rendering
Surface Rendering
3D Image Analysis
Neighborhood of a Voxel
Noise-Removal Method for 3D Fluorescence Images
Depth Attenuation and Correction
Example of Segmentation of a 3D Confocal Microscope Image
Measurement of 3D Objects
Deconvolution of 3D Images Acquired Using Conventional Epifluorescence Microscopy
3D Reconstruction of Tissue from Serial Thin Sections
Other Forms of 3D Imaging
Examples of 3D Image Analysis in Cytometry
Literature Cited
Figures
Tables

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Figures

Figure 10.10.1

The 3D digital image. (A) The image is composed of box-shaped elements, called “voxels,” in contrast to 2D images, which are composed of rectangular elements called “pixels.” The image shown here has 3 voxels in the x direction, 5 in the y direction, and 4 in the z direction; therefore the total number of voxels in this 3D image is 3 × 5 × 4 = 60 voxels. (B) A 2D slice image in the xz plane from the 3D image.

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Figure 10.10.2

Display of 2D slices cut from a 3D image. (A) Schematic representation of a 3D image as a series of 2D slices. (B) Display of a 3D image in the form of a gallery of 2D slices. Shown are 31 slices comprising a 3D image of a solid white object on a black background. (C) Schematic showing the orientation of orthogonal slices through a 3D image. Nonorthogonal slices can also be cut from the image, but they are computationally complex to calculate. (D) The three orthogonal slices that intersect at the center of the 3D image shown in gallery form in B (the xy slice is the same as the slice at row 2, column 8 in panel A). Note that the orthogonal slices reveal that the object is a dumbbell consisting of two superimposed spheres (see Lockett, et al., 1998a, for more details). This is not obvious from the gallery in panel B due to the orientation of the dumbbell with respect to the slices.

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Figure 10.10.3

Generation of projection images in volume rendering. The intensity at coordinate (x¢,y¢) in the projection image is a function of the set of intensities along the line (x¢,y¢, z₁) to (x¢, y¢, z_n) in the 3D image. The same transformation is applied at all coordinates in the xy plane. Often a series of projection images is generated, by incrementally rotating the 3D image about an axis orthogonal to the projection direction.

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Figure 10.10.4

Example of 3D volume rendering of cell nuclei in the epidermis of normal human skin labeled with propidium iodide. (A) Three (nonadjacent) slices from the 3D image. (B) “Maximum intensity” projections at 30° increments through the 3D image. (C) Stereo pair generated from the 3D image. To view, look at the right image with the left eye and the left image with the right eye. (D) Overlay of the pair of images in C after first detecting the edges of the nuclei using a gradient magnitude operator (see unit 10.5).

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Figure 10.10.5

Surface rendering of the 3D image shown in Figure 10.10.4. (A) The surfaces of the nuclei in Figure 10.10.4B without any shading. (B) Shading added by reflecting an imaginary light off the surface, which reveals the orientation of each point on the surface and thus makes it possible to delineate the individual nuclei. (C) By using wire frames to render surfaces, it is possible to see objects inside the nuclei. The white dots inside the nuclei are the centromere of chromosome 1, which has been labeled using fluorescence in situ hybridization (FISH; see Chapter 8).

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Figure 10.10.6

Neighborhood of a voxel. (A) The 3 × 3 × 3 neighborhood surrounding the central voxel, c. The slices have been separated in the z direction for clarity. Voxel c has 26 neighbors, 9 each in the xy planes above and below and 8 in the xy plane containing c. Therefore this neighborhood is called the 26-connected neighborhood. When the coordinates of c are set to the origin (i.e., x, y, z = 0, 0, 0), the 26 neighbors have coordinates: (–1, –1, –1), (–1, –1, 0), , (–1, 0, –1), , (1, 0, –1), , (1, 1, 1) leaving out (0, 0, 0). (B) The 6-connected neighbors of C: (1, 0, 0), (–1, 0, 0), (0, 1, 0), (0, –1, 0), (0, 0, 1), (0, 0, –1).

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Figure 10.10.7

Example of segmentation of a 2D image of fluorescence-labeled cell nuclei. (A) 2D slice image from a 3D image of cell nuclei labeled with propidium iodide (PI; a red dye, rendered in dark gray here). (B) The image in panel A, after thresholding to convert it to a binary image of nuclear regions (white) and background (black). (C) A nuclear region containing a cluster of several nuclei. (D) The region in panel C after application of the binary erosion operator to shrink the region and divide it into multiple regions, each one representing a single nucleus. (E) Labeling of each region in panel D. (F) Dilation of the regions back to their original size.

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Literature Cited

Literature Cited
	Agard, D.A., Hiraoka, Y., Shaw, P., and Sedat, J.W. 1989. Fluorescence microscopy in three dimensions. Methods Cell Biol. 30:353-77.
	Albert, R., Schindewolf, T., Baumann, I., and Harms, H. 1992. Three-dimensional image processing for morphometric analysis of epithelium sections. Cytometry 13:759-765.
	Camacho, P. and Lechleiter, J.D. 1993. Increased frequency of Ca²⁺ waves in Xenopus laevis oocytes expressing a Ca²⁺-ATPase. Science 260:226-229.
	Carlbom, I., Terzopoulos, D., and Harris, K.M. 1994. Computer-assisted registration, segmentation, and 3D reconstruction from images of neuronal tissue sections. IEEE Trans. Med. Image. 13:351-362.
	Castleman, K.R. 1996. Digital Image Processing. Prentice Hall, Englewood Cliffs, N.J.
	Cremer, C., Münkel, C., Granzow, M., Jauch, A., Dietzel, S., Eils, R., Guan, X.Y., Meltzer, P.S., Trent, J.M., Langowski, J., and Cremer, T. 1996. Nuclear architecture and the induction of chromosomal aberrations. Mutation Res. 366:97-116.
	Höfers, C., Baumann, P., Hummer, G., Jovin, T.M., and Arndt-Jovin, D.J. 1993. The localization of chromosome domains in human interphase nuclei: Three-dimensional distance determinations of fluorescence in situ hybridization signals from confocal laser scanning microscopy. Bioimaging 1:96-106.
	Inoué, S. and Spring, K.R. 1997. Video Microscopy. Plenum, London.
	Kaufman, M.H., Brune, R.M., Davidson, D.R., and Baldock, R.A. 1998. Computer-generated three-dimensional reconstructions of serially sectioned mouse embryos. J. Anat. 193:323-336.
	Kay, P.A., Robb, R.A., and Bostwick, D.G. 1998. Prostate cancer microvessels: A novel method for three-dimensional reconstruction and analysis. Prostate 37:270-277.
	Lockett, S.J., Sudar, D., Thompson, C.T., Pinkel, D., and Gray, J.W. 1998a. Efficient, interactive, and three dimensional segmentation of cell nuclei in thick tissue sections. Cytometry 31:275-286.
	Lockett, S.J., Fernandez, C., Rodriguez, E., Wesselmann, U., Bastian, B.C., Sudar, D., Pinkel, D., and Gray, J.W. 1998b. Interactive system for registering adjacent tissue sections. Proc. SPIE 3260:154-161.
	Noordmans, H.J., van der Kraan, K., van Driel, R., and Smeulders, A.W.M. 1998. Randomness of spatial distributions of two proteins in the cell nucleus involved in mRNA synthesis and their relationships. Cytometry 33:297-309.
	Ortiz de Solorzano, C., Rodriguez, E.G., Jones, A., Sudar, D., Pinkel, D., Gray, J.W., and Lockett, S.J. 1999. Automatic nuclear segmentation for 3D thick tissue confocal microscopy. J. Microsc. 193:212-226.
	Rigaut, J.P., Vassy, J., Herlin, P., Duigou, F., Masson, E., Briane, D., Foucrier, J., Carvajal-Gonzalez, S., Downs, A.M., and Mandard, A.-M. 1991. Three-dimensional DNA image cytometry by confocal scanning laser microscopy in thick tissue blocks. Cytometry 12:511-524.
	Rothman, C., Bar-Am, I., and Malik, Z. 1998. Spectral imaging for quantitative histology and cytogenetics. Histol. Histopathol. 13:921-926.
	Thomas, C.F. and White, J.G. 1998. Four-dimensional imaging: The exploration of space and time. Trends Biotechnol. 16:175-182.
	Thompson, C.T., LeBoit, P.E., Nederlof, P.M., and Gray, J.W. 1994. Thick-section fluorescence in situ hybridization on formalin-fixed, paraffin-embedded archival tissue provides a histogenetic profile. Am. J. Pathol. 144:237-243.
	Verbeek, F.J. 1995. Three-Dimensional Reconstruction of Biological Objects from Serial Sections Including Deformation Correction. Ph.D. Thesis, Technical University of Delft, The Netherlands.
	Wartenberg, M., Hescheler, J., Acker, H., Diedershagen, H., and Sauer, H. 1998. Doxorubicin distribution in multicellular prostate cancer spheroids evaluated by confocal laser scanning microscopy and the “optical probe technique.” Cytometry 31:137-145.
	Wessels, D., Voss, E., Von Bergen, N., Burns, R., Stites, J., and Soll, D.R. 1998. A computer-assisted system for reconstructing and interpreting the dynamic three-dimensional relationships of the outer surface, nucleus and pseudopods of crawling cells. Cell Motil. Cytoskeleton 41:225-246.
	Yelamarty, R.V., Miller, B.A., Scaduto, R.C., Yu, F.T.S., Tillotson, D.L., and Cheung, J.Y. 1990. Three-dimensional intracellular calcium gradients in single human burst-forming units-erythroid-derived erythroblasts induced by erythropoietin. J. Clin. Invest. 85:1799-1809.
	Young, H.D. 1962. Statistical Treatment of Experimental Data Waveland Press Prospect Heights, IL.
Internet Resources
	http://genex.hgu.mrc.ac.uk/
The Mouse Atlas and Gene Expression Database Project.
	http://biocomp.stanford.edu/3dreconstruction/index.html
Comprehensive list of image processing and analysis software packages.
	http://www.cyto.purdue.edu
Purdue University Cytometry Laboratories. Provider of the Microscopy CD-ROM containing examples of 2D, 3D, and time-lapse fluorescence microscope images, software, reference and educational material, and microscopy Web sites.