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Using a modified standard microscope to generate virtual slides.

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Using a Modified Standard Microscope to
Generate Virtual Slides
A standard microscope was reconfigured as a virtual slide generator by adding a Prior Scientific H101 robotic stage with
H29 controller and 0.1-␮m linear scales and a Hitachi HV-C20 3CCD camera. Media Cybernetics Image Pro Plus version
4 (IP4) software controlled stage movement in the X-, Y-, and Z-axis, whereas a Media Cybernetics Pro-Series Capture Kit
captured images at 640 ⴛ 480 pixels. Stage calibration, scanning algorithms, storage requirements, and viewing modes
were standardized. IP4 was used to montage the captured images into a large virtual slide image that was subsequently
saved in TIF or JPEG format. Virtual slides were viewed at the workstation using the IP4 viewer as well as Adobe
Photoshop and Kodak Imaging. MGI Zoom Server delivered the virtual slides to the Internet, and MicroBrightField’s
Neuroinformatica viewing software provided a browser-based virtual microscope interface together with labeling tools for
annotating virtual slides. The images were served from a Windows 2000 platform with 2 GB RAM, 500 GB of disk storage,
and a 1.0 GHz P4 processor. To conserve disk space on the image server, TIF files were converted to the FlashPix (FPX)
file format using a compression ratio of 10:1. By using 4ⴛ, 10ⴛ, 20ⴛ, and 40ⴛ objectives, very large gigapixel images of
tissue whole-mounts and tissue arrays with high quality and morphologic detail are now being generated for teaching,
publication, research, and morphometric analysis. Technical details and a demonstration of our system can be found on
the Web at Anat Rec (Part B: New Anat) 272B:91–97, 2003. © 2003 Wiley-Liss, Inc.
KEY WORDS: virtual slide; virtual microscope; microscopy; digital imaging; computer-assisted learning; CAL; medical
curriculum; teaching
Static film images of tissue sections
can currently be replaced in a variety
Mr. Romer is a Systems Engineer in the
Department of Pathology at The Ohio
State University. His research interests
include scanning microscopy technologies, tissue array data and imaging systems, and Web-based medical education technologies. Dr. Yearsley, member
of Sigma Xi, is presently conducting research in Chronic Pathobiology in
Alografts for quantification of fibrosis
and other parameters for The Ohio
State University Department of Pathology. His research includes quantitative
analysis of very large digital images. Dr.
Ayers is Professor of Pathology at The
Ohio State University College of Medicine and Public Health with teaching
and research at University Hospitals,
the James Cancer Hospital, and Solove
Research Institute. Her research interests are in infection-related diseases,
including malignancies, and tissue array
technology, including array digitization.
*Correspondence to: David J. Romer,
Department of Pathology, The Ohio
State University, 129 Hamilton Hall, 1645
Neil Avenue, Columbus, OH 43210. Fax:
614-292-7072; E-mail:
DOI 10.1002/ar.b.10017
Published online in Wiley InterScience
© 2003 Wiley-Liss, Inc.
of settings by computer-based digital images. Such digital images can
be used independently or assembled
together to form a large digital mosaic of a salient histological feature
or tissue whole-mount section. Efforts to produce high-quality digital
gray scale mosaics from microscopic
specimens were successful in the
early 1980s (Silag and Gil, 1985) and
continued into the 1990s from advances in image acquisition and mosaicing techniques (Westerkamp and
Gahm, 1993; Swidbert, 1997). Today,
high-quality digital color mosaics of
histological features and tissue wholemount sections are commonplace and
often referred to as “virtual slides” and
deployed over a network for virtual microscopy (Harris et al., 2001; Heidger et
al., 2002).
The virtual slide is typically viewed
with a virtual microscope application
that enables a microscopist to navigate the virtual slide on a computer
monitor screen in a manner that simulates a standard microscope as
shown in Figure 1. Simulated magnification and navigation capabilities
(pan and zoom) are provided through
a mouse interface, but illumination
and focus capabilities typically are not
available. Systems that create virtual
slides—virtual slide generators—vary
in complexity but, in general, are designed to scan a region of interest
(ROI), programmatically acquire a series of small images from an analog or
digital camera, and tile the small images into a very large montage image.
The montage file size can range from a
few megabytes to several gigabytes.
Until recent years, the large image has
been difficult to create, store, transmit, and view. Advances in image acquisition systems, reductions in disk
storage costs, and improvements in
broadband connectivity have greatly
improved the management, distribution, and viewing of these images.
Browser-based virtual microscopes
are now commonplace, and virtual
slides are now easily shared using a
variety of storage and retrieval media.
Commercial systems such as ScanScope (Aperio Technologies) and
BLISS (Bacus Laboratories, Inc.,
Slide Scanner) acquire overlapping
40⫻. Objectives were chosen to minimize spherical and chromatic aberrations and, thus, produce very flat images for tiling. The 10⫻, 20⫻, and 40⫻
objectives were selected for scanning,
and the 2⫻ and 4⫻ objectives were
selected primarily for ROI setup.
To the microscope we added a Prior
Scientific H101 stage with H29 controller and linear scales with encoders
for positioning the stage with high accuracy and resolution as illustrated in
Figure 2. The stage has an XY travel of
108 ⫻ 73 mm (3 ⫻ 4 inches) and accommodates a traditional glass slide
(75 ⫻ 25 mm). The linear scales have
a resolution of 0.1 ␮m, which we believed necessary to accommodate the
0.37 ␮m optical resolution of the 40⫻
objective. We chose Media Cybernetics Image Pro Plus Version 4 (IP4) to
control stage movement in the X-, Y-,
and Z-axis. An auto focus option was
available for the Z-axis but not implemented.
Image Acquisition
Figure 1. Screen snapshots of a virtual slide and browser-based virtual microscope. A: Lowpower (1.25⫻) view of appendix, also illustrating mouse-driven navigation window in upper
right corner and panning cursor on large image. B: Change in magnification to 20⫻ and
navigation to goblet cells as indicated by the small yellow square in the navigation window.
images and use intelligent pattern
recognition techniques to stitch the
images together. The system we developed, however, relies solely on precise
stage positioning to acquire nonoverlapping images. We discuss the components
used for converting a standard microscope, as well as the tools we selected for
viewing, annotating, and analyzing virtual slides.
Microscope and Robotic Stage
We selected an Olympus BX51 microscope with the following objectives:
Plan 2⫻ and 4⫻, U Plan Apochromat
10⫻ and 20⫻, and U Apochromat
The Hitachi HV-C20 3CCD camera is
a three-chip (red, green, blue) chargecoupled device (CCD) and was selected for its simplicity, image quality,
and history of use on our video microscopes. The camera was coupled to
the microscope with a 0.5⫻ C-mount
adapter. We interfaced it to a Media
Cybernetics Pro-Series Capture Kit to
capture images at 640 ⫻ 480 pixels.
The camera was set to 1/2,000-second
manual shutter, auto gain was disabled, and the white balance was set
to 3,200 K. Gross adjustments to the
illumination were made by means of
neutral density filters and minor adjustments were made by means of the
lamp intensity control near 3,200 K.
The aperture iris diaphragm (located
on condenser, directly below slide)
was set to approximately 80% of the
numerical aperture of the objective
used for scanning. The field iris (located
on base) was set to produce the sharpest image without producing vignetting. This setup provided good contrast
and helped moderate CCD blooming
effects, which can occur when the illumination is too great, causing CCD
saturation. The workstation consisted
of a 1.4 GHz P4 processor, 2 GB RAM,
Figure 2. Left: Modified standard microscope with camera (A), 0.5⫻ adapter (B), X-axis motor (C), Y-axis motor (D), Z-axis motor (E), linear
scales (F), slide leveling assembly and stage (G), and joystick (H). Right: X-axis linear encoder and scale strip for position feedback (A), X-axis
left/right over travel limit switches (B), glass slide holder (C), and nylon screw (D), for stage leveling (one of four). [Color figure can be viewed
in the online issue, which is available at]
60 GB disk storage, and Windows
2000 was chosen as the operating system.
Images captured during the scanning process described below required additional manipulations before insertion into the montage.
Because the border pixels in the 640 ⫻
480 image were not reliable, we developed macros using the IP4 Visual Basic language to crop the acquired
640 ⫻ 480 image down to 632 ⫻ 472
during the scan. We added a background correction macro to remove
image artifacts such as vignetting and
variations in color and luminance
from each image as illustrated in Figure 3. Both macros were implemented
immediately after image acquisition
and before tiling. Additional information on the photomicrographic technique we chose can be found at the
Olympus Microscope Resource Center at
Camera Alignment (Squaring)
Of paramount importance to image
acquisition is the alignment between
the camera and stage as shown in Figure 4. Squaring the camera with the
stage is an iterative process that involves rotating the camera slightly,
scanning an ROI to create a montage,
inspecting the montage image, and
then iterating the process until satisfactory tile-to-tile alignment is observed. To facilitate alignment, we
used a 40 line/mm (12.5 ␮m line
width) Ronchi ruling, which is a precision glass target with equally spaced
parallel lines typically used for evaluating resolution and field distortion.
By using a 40⫻ objective, we scanned
a small portion of the Ronchi ruling
and inspected the montage image for
indications of vertical skewing. We
then rotated the camera slightly and
iterated the process until skewing was
Stage Leveling
The stage must be nearly level from
front-to-back and left-to-right to produce images that are tightly focused.
If the stage slopes excessively from
left-to-right, then an acquired image
will be in focus at one side and out of
focus at the opposite, which creates a
problem during tiling. The stage does
not need to be perfectly level, how-
ever, because programmatic focusing
is performed during the scan to account for minor variations in level as
well as tissue flatness.
To level the stage, we used a Ronchi
ruling and first observed the quality of
focus at the far four corners of the
ruling area, which was approximately
25 ⫻ 12 mm. By using only the Z-axis
control, we focused on one of the four
corners. We then moved the stage approximately 25 mm along the X-axis
and focused by adjusting only the
stage leveling screws. We moved the
stage approximately 12 mm along the
Y-axis and again focused by adjusting
only the stage leveling screws. With all
four corners reasonably in focus, we
moved the stage again to each of the
four corners and focused using the
Z-axis control, recording the Z-axis
position data for each corner. From
these data, we calculated the stage
slope from front-to-back and also leftto-right. We achieved a slope of approximately 1 ␮m/mm in each direction. Thus, for our 40⫻ objective with
a camera X-axis field of view (FOV) of
0.39 mm, the variation in focus across
a static 640 ⫻ 480 image was deter-
5). First, to determine approximate X
and Y displacement values for each of
the objects, we used a 2.00-mm stage
micrometer with 0.01-mm divisions,
captured a 640 ⫻ 480 image, and then
measured the camera X-axis FOV (Table 1). For our 10⫻, 20⫻, and 40⫻
objectives, X-axis FOVs were approximately 1.24 mm, 0.63 mm, and 0.31
mm, respectively. For a cropped
632 ⫻ 472 pixel image at 10⫻, this
corresponded to a displacement of approximately 0.002 mm/pixel (2 ␮m/
pixel). By using this as a seed value in
the scanning algorithm, a preliminary
montage of the stage micrometer was
produced. After inspection of the
montage seam quality, the seed value
was refined and the process was iterated until the seam was minimized.
For 10⫻, the final value for X-axis displacement was determined to be 1.944
␮m/pixel, or 1.228 mm/tile. The process was repeated for the 20⫻ and
40⫻ objectives and displacements
were found to be 0.972 and 0.485 ␮m/
pixel, respectively. At 40⫻, seams that
were less than 1.0 ␮m were obtained,
as shown in Figure 5.
Scanning Algorithms
Figure 3. Background correction removes artifacts from acquired image before tiling. Top
image (A) is a mosaic of tiles that have not been corrected for variations in color and
luminance (note vignetting). Bottom image (E) is a mosaic of tiles that have been corrected
using the “normal” background image shown in the center series of images (B–D). This
middle series of images demonstrates the correction process, which uses a background
image to correct each newly acquired image before tiling. Images in this figure were
purposely underexposed to demonstrate efficiency and range of correction algorithm.
[Color figure can be viewed in the online issue, which is available at www.interscience.]
mined to be 0.39 ␮m, which was unnoticeable in the montage image.
Stage Calibration
Because images were not acquired using the overlap method, our system
was very dependent on precise stage
positioning for capturing adjacent images. An iterative process of scanning
and inspection of the montage seams
determined precise XY displacement
values for each objective (see Figure
Although IP4 includes menus for
scanning images and creating montages, we created a user interface and
scanning algorithm using IP4 macros
to produce montages more efficiently
and with better quality. Our scanning
algorithm produced raster scans over
rectangular ROIs with no overlapping
among images.
To perform a scan, the user clicks
on a menu item and follows the directions provided through a short series
of dialog boxes. First, the user defines
an ROI by moving the stage to the
four edges of a feature or wholemount. After this, a properly sized
blank IP4 workspace is created for the
large montage file. Next, the user focuses at three different points on the
feature to locate a plane in the Z-axis
that best fits the feature. The XYZ position values are recorded at the three
points and applied to an equation that
returns a new value of Z for any XY
position during the scan. We refer to
this as programmatic focusing rather
than auto focusing, which is known to
increase the scan time and sometimes
produce focusing artifacts in the final
Zoom Server and MicroBrightField’s
Neuroinformatica viewing software,
which also catalogs images in a
MYSQL database. The images were
served from a Windows 2000 platform
supported with 2 GB RAM, 500 GB of
disk storage, and a 1.0 GHz P4 processor. The Neuroinformatica software
also allows users to annotate virtual
slides with drawings and text by
means of the virtual microscope interface. Additional information on the
viewing software can be found at the
MicroBrightField web site
Figure 4. Improper alignment between camera and stage (A), resulting in poor tiling of nine
adjacent images of a Ronchi ruling (B) and histological feature (C) scanned at high power.
Proper alignment of same (D–F). [Color figure can be viewed in the online issue, which is
available at]
montage. Finally, the user is asked to
move the stage to an area void of specimen and uniformly lighted to acquire
an image for background correction.
After the last step, the scan commences in a raster mode from right to
left and bottom to top. Stage translations are not continuous and are descretized according to the camera FOV
for the scanning power. For example,
scanning a 20 ⫻ 20 mm ROI with a
10⫻ objective causes the stage to
move 1.228 mm in the X-axis and then
pause briefly for image acquisition before moving again. In this example,
the process would be repeated 17
times along the X-axis, because 20 is
not evenly divisible by 1.228, and fractional frames (i.e., 16.3) are not possible. Each acquired image, thus, is stable and suitable for cropping,
correcting, and copying into the mon-
tage workspace with no separation between images. At completion, the
montage file is saved as a TIF or
We also developed a scanning algorithm that does not produce a montage in the IP4 workspace but instead
acquires images and saves them to
disk with a row– column name, like
001-001.JPG and 001-002.JPG. These
images have been cropped and background-corrected and can be tiled
outside of the IP4 environment.
Presentation and Viewing
The IP4 workspace is used to view the
virtual slide at the workstation, but
Adobe Photoshop and Kodak Imaging, integral to Windows 2000, can
also be used. To present virtual slides
on the Internet, we used the MGI
Adding the Prior robotic stage to the
standard microscope required no special tools and was accomplished in a
few minutes. The IP4 stage control
software installed quickly, and after
studying the IP4 manuals, the first virtual slide at 10⫻ was generated a few
hours later using standard IP4 menus.
The stage hardware and IP4 software
were designed for compatibility and
ease-of-installation and use. The first
virtual slide at 40⫻ was generated several hours later, due to the iterative
procedure of leveling the stage and
determining the proper stage calibration (pixel/step), which are both very
critical at 40⫻.
Our virtual slide generator continues to produce very high quality images from tissue sections, including
tissue arrays. Camera alignment is
difficult to setup initially but does not
appear to drift unless the camera is
jarred severely or the equipment is
moved. Stage calibration, on the other
hand, does drift and requires periodic
adjustment to a software parameter if
high-power objectives are used for diagnostic or image analysis purposes.
Specifically, we have observed that
large ambient temperature swings of
10 –20°F affect the thermal expansion
of sensitive stage and camera components and, thus, affect the resolution
values used for stage calibration. For
example, for our 40⫻ objective, we
have observed the stage resolution
varies between 0.490 ␮m/pixel and
0.475 ␮m/pixel. Future plans include
automating the stage calibration procedure by using a stage micrometer
and IP4 image analysis routines to determine stage resolution values pro-
Figure 5. Montage of images from a 2.00-mm stage micrometer at high power (40⫻).
Top image was generated with a stage calibration value set too low, resulting in
separation of tiles at approximately 0.27 mm as indicated by an additional division line
between 0.25 and 0.30 mm. Middle image shows correct montaging. Bottom image
shows overlapping of tiles at 0.27 mm, and loss of a division line between 0.25 and 3.0
mm. [Color figure can be viewed in the online issue, which is available at www.]
grammatically and at the discretion of
the operator. The effect of variations
in the stage calibration is shown in
Figure 5.
Because our installation of IP4
could not create montages greater
than 220 megapixels— corresponding
to 831 mm2 at 10⫻, 207 mm2 at 20⫻,
and 51 mm2 at 40⫻—we developed an
IP4 macro to save individual images
to disk and then developed a program
to tile these images outside of the IP4
workspace. By using this approach,
the size limitation of the montage depends only on available storage capacity.
The time it takes to scan a feature
depends not only on the size of the
ROI but also on the objective used as
presented in Table 1. We observed
scan coverage rates of 33.6 mm2/min
at 10⫻, 9.0 mm2/min at 20⫻, and 2.4
mm2/min at 40⫻. Doubling the scanning power roughly quadrupled the
number of acquired images, the time
to scan the images, and the amount
of required storage. Scanning an
ROI of 20 ⫻ 20mm would require
approximately 12 minutes at 10⫻, 44
minutes at 20⫻, and 166 minutes at
40⫻. The same ROI would produce
uncompressed RGB image files of
318 MB at 10⫻, 1,207 MB at 20⫻
(1.2 GB), and 5,101 MB (5.1 GB) at
40⫻. Scanned images are initially
saved in TIF format, which has a size
limitation of approximately 4 GB.
Our experience has been limited to
file sizes less than 1 GB. To conserve
disk space on the Internet image
server the TIF file is converted to a
FlashPix (FPX) file using a compression ration of 10:1. Further reduction in file size may be realized in the
future with JPEG 2000, which promises similar quality images but with
improved compression ratios of
20:1. Additional information on
JPEG 2000 can be found at http://
Creating montages of blood
smears or targets that are not flat
presents a problem, because the
scanning algorithm uses programmatic focusing based on 2D planar
geometry. We have been successful
in scanning only sloping sections
that “rise” or “fall” and are mostly
planar; however, these are relatively
small ROIs. Future plans include
modifying the focusing method to
accommodate nonplanar targets by
constructing a 3D surface mesh of
the target as a basis for focusing.
A virtual slide generator can produce
virtual slides for many different uses.
First, in conjunction with the virtual
microscope (Ferreira et al., 1997), it is
considered an emerging learning aid
in histology and pathology laboratory
instruction (Heidger et al., 2002).
Teaching with conventional glass histology slides is difficult, because they
are breakable, difficult to distribute,
TABLE 1. Virtual Slide Generator Characteristics
20⫻20 mm
ROI Scan
Time (min)
20⫻20 mm
File Size(2)
X-axis Field
of View
(FOV) (um)
Camera is couple to microscope via 0.5X adapter.
Uncompressed file size calculated from resolution (um/pixel) for 3-color (RGB)
and fade over time. A virtual slide image, on the other hand, is an indestructible digital medium with extremely stable image quality and is
easily accessible by means of the Internet (Harris et al., 2001). Second,
the virtual slide can be used in telepathology, where published studies indicate that the degree of concordance
between viewing digital microscopy
images and viewing histology through
conventional microscope eyepieces is
high enough for this to be feasible (Leong and McGee, 2001). Third, if the
ROI is the whole-mount, then the corresponding virtual slide will be a very
high-resolution image of a low-power
view of the overall tissue architecture,
which is difficult to obtain using normal photomicrographic techniques,
but which is highly desirable for presentation, publication, and teaching.
Fourth, because the virtual slide is
rich in morphologic detail, it is well
suited to quantitative morphometric
analysis using standard analysis tools
like those available in IP4. For example, success has been achieved scanning specimens at 40⫻, to produce
large montage images of 14,000 ⫻
11,000 pixels in approximately 15
minutes. When viewed at 10% of original size, there is enough detail
present to subsequently select an area
for further analysis. From the selected
area, 5 to 10 small ROIs are determined and subimages of 640 ⫻ 480
pixels are extracted using cut-andpaste, and saved for morphometric
analysis. By using macros written in
IP4 Basic, data collection and quantitative analysis usually takes less than
a minute per ROI. Specific analysis
has been performed on murine heart,
lung, liver, and skin allograft tissues
to study the amount of fibrosis, macrophage infiltration, and/or arterial
hyperplasia. Overall image analysis
using the VSG is less time consuming
and much more accurate than visually
rating each specimen manually.
Finally, the authors are deploying
virtual microscopy in the research setting to study and document virtual
slides of tissue arrays. Investigators
will be able to navigate to a core of
interest, store, and retrieve visual as
well as bioinformation from a database. Sharing images and reviewing
interpretations on-line with collaborators and reviewers are of special interest.
The system continues to evolve
while it produces very useful virtual
slides. We recently used virtual slides
at the “2nd Annual Update Course in
Surgical Pathology” sponsored by the
Our virtual slide
generator continues to
produce very highquality images from
tissue sections including
tissue arrays.
Ohio State University Department of
Pathology in Columbus, OH, August
25–27, 2002 (Romer and Suster,
Medium-power images (10⫻) are
the easiest and fastest to acquire and
produce excellent teaching slides as
well as high-quality megapixel images
for presentation, publication, and research. The virtual slide generator has
been in operation since November
2001 and has not required any mechanical adjustments, other than
those required after equipment relocation. The Prior stage and IP4 software cost under $20,000 and, once
properly configured, produce images
comparable to commercial systems
costing over $60,000. Addition of
these components can extend the usefulness of a standard video microscope by enabling educators and researchers to acquire very large digital
images for many different purposes.
We thank all those who have helped in
the design of our system, and in particular Dr. Saul Suster, Director of Anatomic Pathology, for enthusiastically
supporting our technology by using
virtual slides at national conferences,
and also Dr. Charles Hitchcock, Director of Medical Education, for identifying interesting slides to evaluate and
for introducing students to this technology. This project was supported in
part by a grant (CA66531-07) from
Ferreira R, Moon B, Humphries J, et al.
1997. The virtual microscope. Proceedings AMIA Annual Fall Symposium. p
449 – 453.
Harris T, Leaven T, Heidger P, Kreiter C,
Duncan J, Dick F. 2001. Comparison of a
virtual microscope laboratory to a regular microscope laboratory for teaching
histology. Anat Rec 265:10 –14.
Heidger PM, Dee F, Consoer D, Leaven T,
Duncan J, Kreiter. 2002. Integrated approach to teaching and testing in histology with real and virtual imaging. Anat
Rec 269:107–112.
Leong FJ, McGee JO. 2001. Automated
complete slide digitization: A medium
for simultaneous viewing by multiple pathologists. J Pathol 195:508 –514.
Romer D, Suster S. 2003. Use of virtual
microscopy for didactive live-audience
presentation in anatomic pathology.
Anal Diag Pathol 7(1):67–72.
Silage DA, Gil J. 1985. Digital image tiles: A
method for the processing large sections.
J Microsc 138(Pt 2):221–227.
Swidbert RO. 1997. Acquisition of highresolution digital images in video microscopy: Automated image mosaicking
on a desktop microcomputer. Microsc
Res Tech 38:335–339.
Westerkamp D, Gahm T. 1993. Non-distorted assemblage of the digital images
of adjacent fields in histological sections.
Anal Cell Pathol 5:235–247.
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