Woman Prone

Marching Through the Visible Woman

The National Library of Medicine continues its Visible Human project with the Visible Woman. This paper describes on-going results in processing the CT data using the methodology described in Marching Through The Visible Man.

Make Your Own Visible Woman shows how to use the Visualization Toolkit to make and render surface models of the Visible Woman.

For more examples of 3D medical imaging done in our laboratory go to our Scientific Movie Library.



- Introduction
- The Visible Woman
- Results
- References



In 1989, the National Library of Medicine (NLM) began an ambitious project to create a digital atlas of the human anatomy. The NLM Planning Panel on Electronic Image Libraries [1] recommended a project to create XRAY Computed Tomography (XRAY-CT), Magnetic Resonance Imaging (MRI) and physical sections of a human cadaver. The project is called "The Visible Man." Another cadaver, that of a 59 year-old woman, "The Visible Woman", has just been released.


The Visible Woman

The data is from a 59-year-old Maryland woman who willed her body to science. The fresh computed tomography data was acquired with varying in-slice resolution, each slice 1 mm apart. The physical cross-section data has the same .3 mm in-slice resolution, but, in contrast to the Visible Man's 1mm slice thickness, her slices are .3 mm apart.

The Fresh CT Data Set


We obtained the fresh CT data using ftp via the Internet. Internet access is available to users that sign a license agreement with NLM. The data is stored one slice per file and the files have been compressed using the unix compress program. Uncompressed, each slice is 512 x 512 x 16 bits with a 3416 byte header. The format of the headers is General Electric Genesis described in the Medical Image Format FAQ. The image header contains among other things the table position and field of view. These are important quantities when working with this data since the spacing between the slices and the pixel size changes several times throughout the data set. The slices are named as follows: c_vfxxxx.fre where xxxx is the location in mm's of the slice. There are 1734 slices in the fresh CT data set using about 480 megabytes of disk storage (compressed).

On a Unix system, these files can be converted into files without headers with the following script:

set n = 1001
set m = 1
while ($n <= 2734)
zcat c_vf$n.fre.Z | dd ibs=3416 skip=1 | compress - >slice.$m.Z
@ n = $n + 1
@ m = $m + 1

Description of the CT data from headers

The first step to understand the data was to print the header information. In particular, we need to know the size of the pixels, and the distance between each slice.

The fresh CT data was acquired in several sections with varying pixel size. The following table summarizes the data.
Summary of the Fresh CT Data
Section Slice Range FOV Pixel Size Spacing


This work was done on an Onyx Reality Engine 2 (Silicon Graphics, Mountain View, CA) with the following configuration:

2 150 MHZ IP19 Processors
CPU: MIPS R4400 Processor Chip Revision: 5.0
FPU: MIPS R4010 Floating Point Chip Revision: 0.0
Data cache size: 16 Kbytes
Instruction cache size: 16 Kbytes
Secondary unified instruction/data cache size: 1 Mbyte
Main memory size: 256 Mbytes, 2-way interleaved
RealityEngineII Graphics Pipe 0 at IO Slot 3 Physical Adapter 2 (Fchip rev 2)


The Reality Engine was running Irix 5.3. We used a variety of software tools we call the Research Workstation all developed in-house. All of the software works with 16-bit medical images. These are described in the Visible Man companion to this paper. We did apply one new operation to the data after decimation. We smoothed the decimated triangle vertices with a Laplacian smoothing algorithm.

The following table contains triangle counts and timings for each of sections.
Summary of Visible Woman Surface Extraction
Section Slice Range Skin Original Tri's Marching Time (sec) Skin Decimated Tri's Decimate Smooth Time (sec) Bone Original Tri's Marching Time (sec) Bone Decimated Tri's Decimate Smooth Time (sec)
0 1001-1209 1,430,794 195 164,701 623 2,109,874 213 324,411 968
1 1210-1227 151,749 16 22,067 65 39,090 9 13,020 23
2 1228-1249 138,691 16 22,069 59 85,232 13 25,701 46
3.1 1250-1450 783,169 117 76,529 325 1,916,114 174 742,551 1252
3.2 1450-1650 1,026,719 133 125,423 443 1,327,550 156 553,644 804
3.3 1650-1850 929,727 124 87,867 392 1,743,998 166 728,827 1112
3.4 1850-1985 539,200 79 60,166 256 526,876 80 248,065 371
3.5 1986-2106 493,642 72 77,665 226 65,563 49 11,600 31
4 2107-2110 14,408 3 2,250 7 3,587 2 718 2
5 2111-2117 19,743 4 3,160 10 5,624 4 1,387 3
6.1 2118-2318 767,802 359 93,068 336 1,097,398 144 480,409 722
6.2 2318-2518 532,738 103 59,791 230 188,145 85 30,114 89
6.3 2518-2734 605,383 114 58,531 243 805,071 123 238,605 478
Totals 1734 6,650,596 1335 853,287 3215 9,914,122 1218 3,399,052 5901



The first experiments were done using 209 slices in the head. The field of view was 250 mm resulting in a pixel size of .48828 mm pixels. Slices were 1 mm apart.

Bone and Skin

We used a threshold of 600 for skin and 1224 for bone.

Head Skin Head Skin Head Skin Head Skin Head Skin
Head Bone Head Bone Head Bone Head Bone Head Bone
Head Skin/Bone Head Skin/Bone Head Skin/Bone

We continued by reconstructing the entire skin and bone model. We used seeds to selected connected components before applying the Marching Cubes algorithm. We also split sections 3 and 6 into smaller pieces to reduce memory requirements during model decimation and smoothing.

Full Body Surface Models
Head Skin Head Skin Head Bone Head Bone
Chest Skin Chest Skin Chest Bone Chest Bone
Stomach Skin Stomach Skin Stomach Bone Stomach Bone
Thighs Skin Thighs Skin Thighs Bone Thighs Bone
Knees Skin Knees Skin Knees Bone Knees Bone
Feet Skin Feet Skin Feet Bone Feet Bone

Some full body models:
Full Body Skin Full Body Skin Full Body Skin Full Body Skin
Full Body Bone Full Body Bone Full Body Bone Full Body Bone
Full Body Skin/Bone Full Body Skin/Bone Full Body Skin/Bone Full Body Skin/Bone

A little java.



  1. National Library of Medicine (U.S.) Board of Regents. Electronic imaging: Report of the Board of Regents. U.S. Department of Health and Human Services, Public Health Service, National Institutes of Health, 1990. NIH Publication 90-2197.
  2. Cline, H. E., Dumoulin, C. L., Lorensen, W. E., Hart, H. R., and Ludke, S., "3D Reconstruction of the Brain from Magnetic Resonance Images Using a Connectivity Algorithm," Magnetic Resonance Imaging, vol. 5, no. 5, pp. 345-352, 1987.
  3. Lorensen, W. E. and Cline, H. E., "Marching Cubes: A High Resolution 3D Surface Construction Algorithm," Computer Graphics, vol. 21, no. 3, pp. 163-169, July 1987.
  4. Schroeder, W., Lorensen, W., Montanaro, G. and Volpe, C., "Visage: An Object-Oriented Scientific Visualization System," in Proceedings of Visualization '92, IEEE Press, October 1992, pp. 219-226.
  5. Schroeder, W. J., Zarge, J., and Lorensen, W. E., "Decimation of Triangle Meshes," Computer Graphics, vol. 26, no. 2, pp. 65-70, August 1992.
  6. Taubin, G., "Curve and Surface Smoothing without Shrinkage," IBM Research Report RC-19536, September, 1994.
  7. Taubin, G., "A Signal Processing Approach to Fair Surface Design," Computer Graphics Proceedings, pp. 351-358, August 1995.


Questions / Comments

James Miller (millerjv@crd.ge.com)

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