Journal of Histochemistry and Cytochemistry, Vol. 47, 1307-1314, October 1999, Copyright © 1999, The Histochemical Society, Inc.


ARTICLE

Analysis of Stained Objects in Histological Sections by Spectral Imaging and Differential Absorption

Richard L. Ornberga, B. Mark Woernera, and Dorothy A. Edwardsa
a Monsanto Company, St Louis, Missouri

Correspondence to: Richard L. Ornberg, Monsanto Co., 700 Chesterfield Parkway North, GG3G, St Louis, MO 63198.


  Summary
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Summary
Introduction
Methods and Materials
Results
Discussion
Literature Cited

We describe a new light microscopic imaging system and method to perform high through put color image analysis on histological tissue sections. The system features a computer-controlled, random-access liquid crystal tunable filter and high-resolution digital camera on a conventional brightfield microscope. For any combination of stains, the method determines the spectral transmittance of each stain on the slide and selects two or more wavelengths at which the differential absorption between stain and counterstain is greatest and the exposure time is reasonably short. Flatfield corrected digital images at these wavelengths are acquired and divided to produce a gray scale ratio image. The ratio image is calculated such that the stained features of interest are highlighted above a uniform background and the counterstained features are highlighted below background. Image threshold procedures using either visual inspection or a threshold value determined by the image mean intensity and standard deviation are used to segment the stained features of interest for subsequent morphometry. Results are presented for peroxidase–AEC-labeled tumor tissue and trichrome-stained biomaterial implant tissues. In principle, the method should work for any combination of colored stains. (J Histochem Cytochem 47:1307–1313, 1999)

Key Words: digital image analysis, color image analysis, spectral analysis, image segmentation, differential absorption, morphometry


  Introduction
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Summary
Introduction
Methods and Materials
Results
Discussion
Literature Cited

Color is an important factor in defining structures in biological science. The fields of histology and pathology are founded on the use of special dyes and staining procedures that label cells or structures of interest with a defining color. Examples include the use of special stains for specific biochemical groups, e.g., trichrome stain for matrix and cell tissue, and the use of chromogenic substrates in enzyme linked-immunochemistry, e.g., immunoperoxidase labeling, and reporter enzyme constructs in transfected cells, e.g., ß-galactosidase-transfected cells. Hence color image analysis, (CIA), in which colored objects of interest are isolated or segmented from surrounding structures for subsequent measurement, is becoming an increasingly important tool in modern pathology and cell biology. At present, CIA-based measurements can be performed on most commercial computer image systems equipped with color video imaging systems. Color images are captured and displayed as a set of three black-and-white images collected with red (R), green (G), and blue (B) light, i.e., at wavelengths of approximately 630 nm, 545 nm, and 435 nm, respectively. These wavelengths are chosen to match the spectral response of the human eye. A region of interest stained with a given color can, in principle, be determined by simultaneously thresholding the three images to extract image pixels having a unique set of RGB intensities that correspond to the object of interest. Alternatively, the RGB images can be converted to three images that specify the hue (H), saturation (S), and intensity (I) of color image (Russ 1990 ; Pratt 1991 ; Castleman 1998 ). Hue describes the color (e.g., red, orange, yellow, green, purple), saturation describes the amount of color, and intensity describes the light and dark variations. Analogous to RGB analysis, colored objects can be segmented from images by selecting pixels having a unique range of HSI values. More elaborate algorithms have been developed to use combinations of these components to generate unique descriptors of stained objects for subsequent routine gray scale image segmentation (Smolle 1996 ).

In principle, each of these methods should provide adequate color image segmentation and, for optimally stained tissue sections, these work reasonably well. However, several technical and practical problems arise with such systems. First, most color cameras use a CCD sensing system and produce a color-encoded analog electronic signal that is digitized by a computer videoboard into R, G, and B pixel intensities. The fidelity and day-to-day consistency of the RGB values for a given color can be variable. Section-to-section variability, in conjunction with or without the use of automatic gain control on the camera, often results in such variability and dictates operator intervention for consistent analysis. Because the color of a stained object is determined by the stain's spectral transmittance and the detector's spectral sensitivity, it is possible that dyes having different spectral properties could produce similar sets of RGB values and be indistinguishable in subsequent analysis of the digital image. Finally, the spatial resolution of RGB cameras is lower than that of monochrome CCD cameras because three pixels are interleaved to make one pixel in the digital image. Hence, despite evidence that computer-based image analysis has distinct advantages over visual scoring methods (Kohlberger et al. 1997 ; Tomatis et al. 1998 ), CIA with many of the current systems is tedious and is not as widely utilized as it could be.

Here we describe a new imaging system and method that takes advantage of the spectral features of various stains used in histological sections to segment features of interest for subsequent morphometric measurement. The system uses a liquid crystal tunable filter in conjunction with a high-resolution CCD camera on a standard light microscope to collect images at two or more defined wavelengths that are appropriate for a given stain. These wavelengths are selected such that their ratio provides a new image in which the stained objects of interest are highlighted above a uniform background. Routine gray scale segmentation routines are then used to segment and measure the features of interest. The method is well founded on the physical principles of light absorption and is simple and straightforward to perform. The method has been successfully applied to a wide range of histological stains and immunolabeling reaction products and has proved capable of detecting any color stained object. For the sake of brevity, we describe the analysis of two stains routinely used in histopathology, the analysis of immunocytochemically stained angiogenic endothelial cells in tumor tissues and the analysis of trichrome-stained collagen matrix in a biomaterial implant site.


  Methods and Materials
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Summary
Introduction
Methods and Materials
Results
Discussion
Literature Cited

Histological Methods
Tissues for demonstrating the use of differential absorption for special stains were fixed in formalin and embedded in paraffin according to routine procedures. The trichrome stain for collagen and extracellular matrix (Sigma; St Louis MO) used the method of Gomori 1950 . The principal dyes of this stain include Wiegert's hematoxylin, chromotrope 2R, and aniline blue. Tumor tissues for immunohistochemistry were generated from implants of rat Leydig tumor cells in female BALB/c SCID mice as described elsewhere (Carron et al. 1998 ). Tumors were fixed in a nonaldehyde fixative, NOTOX (Cel-Tek; Des Plaines, IL) and processed through paraffin. Vascular tissue was immunolabeled with pan-endothelial cell primary antibody, MECA-32 (PharMingen; San Diego CA), followed by detection with a biotinylated secondary antibody, peroxidase-conjugated streptavidin, and aminoethylcarbazole (AEC) chromogen (Zymed Laboratories; South San Francisco CA).

Imaging System and Procedures
The imaging system consisted of a low light level CCD camera having 1317 x 1035 pixels by 4096 gray levels (Photometrix; Tucson AZ) coupled to a liquid crystal tunable filter (Cambridge Research Instrumentation; Cambridge MA). The filter is based on the Lyot birefringent interferometer and provided continuous, random access wavelength selection from 400 to 720 nm with a stated wavelength-dependent bandwidth of 0.125 times the central wavelength (Morris et al. 1994 , Morris et al. 1996 ). Images collected over this wavelength range were free of distortion and wavelength-dependent translations or shifts. The camera and tunable filter were controlled by a Silicon Graphics O2 workstation and Isee software (Inovision; Raleigh, NC). All image acquisition, including tunable filter, camera control, and image processing, was performed using Isee software routines. However, a number of suppliers of alternative image software for the tunable filter and other processes described below are available; see Cambridge Research Instrumentation website, www.cri-inc. com, for details. Images were acquired through either an inverted Nikon Diaphot microscope or an Olympus AX-70 Provis light microscope equipped with planapochromat objectives and conventional halogen light sources operated at a standard 9.0 V. A daylight color balance filter and appropriate neutral density filters were used in the illumination path to keep exposure times between 0.05 and 20 sec.

The procedure to collect images of color objects for subsequent segmentation consisted of four separate operations: (a) determination of the transmittance of the stained object for selection of the appropriate wavelengths for subsequent image acquisition; (b) collection of flatfield correction images at the appropriate wavelengths; (c) collection of images from tissue sections at the chosen wavelengths; and (d) image processing and segmentation.

For any given stained section, the transmittance spectra of the stained deposit of interest were determined using the standard formula,

where x', y' are coordinates for a stained area or object and xo, yo are coordinates for a white or unstained space in the section. Using the Isee software, images from five stained regions or structures, five regions that had counterstain only (hematoxylin-stained cell nucleus), and five white spaces (Figure 1) were obtained by collecting successive images every 5 nm from 400 nm to 720 nm. Typically, these regions contained 200 or more pixels and the mean intensity of each was recorded along with the wavelength. The exposure times for these images were automatically adjusted at each wavelength to keep the white space intensity between 2200 and 3800 U. Transmittance data of the stained and counterstained regions were calculated and plotted vs wavelength using Microsoft Excel.



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Figure 1. High-magnification view of AEC–peroxidase-immunostained endothelial cells in a tumor section that illustrates regions selected from transmittance spectra determinations. (a) The immunostained region of interest. (b) The counterstained nuclei. (c) The white space region containing no stain. The mean intensities from such regions were collected from serial spectral images as described in the text. The transmittance of a particular stain combination was then calculated as described and is plotted in Figure 2.

Spatial inhomogeneities in the illumination system and in the transmittance of the tunable filter required that all acquired images be corrected according to the following equation,

where Iraw is the uncorrected image, Iflatfield is the flatfield image, and Idark is the dark current image obtained with no illumination of the CCD chip. The dark current image was a constant 175 to 178 gray U and the flatfield mean intensity was typically 3000–3200 U. Flatfield images were acquired at the predetermined wavelengths by imaging a coverslipped slide containing no section. Separate flatfield images were collected for each microscope objective at the beginning of an analysis session and were used through out the session as long as the imaging and illumination conditions remained constant.

Images of stained sections were collected at the desired magnification and wavelengths with the same exposure conditions were used for collecting the flatfield images described above. In a sequence of automated steps, images at desired wavelengths were acquired, flatfield-corrected, divided, and the quotient was multiplied by a scalar value of 3000 to form a scaled ratio image having 16 bit or 65,536 gray values. The ratio image was either stored to disk or immediately processed by segmentation and morphometric routines. Typically, the ratio image was set up to render the stained areas bright, the counterstained areas dark, and all other regions a mid-level gray. For a scalar value of 3000, the bright areas had intensities above 3500 and the dark areas were below 2400. Using similar values or as otherwise noted in the text, routine segmentation methods using brightness thresholding were used to make binary images for routine morphometric analysis. The speed of the work station and the simplicity of the software typically allowed the collection of two or three images per minute for high through put analysis.


  Results
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Summary
Introduction
Methods and Materials
Results
Discussion
Literature Cited

Transmittance Spectrum Determination
The spectral imaging capability of the tunable filter allows one to treat each pixel in an image as a cuvette in the conventional spectrometer. In Figure 1, typical areas of a white space or blank cuvette, a background or counterstain space, and the stained region of interest are illustrated. Using a looping software routine, successive images from 410 nm to 720 nm in 5-nm increments were collected and mean intensities of five white space areas, five stained areas and five counterstained areas, were tabulated and averaged. The transmittance spectra were calculated as described above. Curves of AEC–peroxidase reaction product and trichrome stain are shown in Figure 2.



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Figure 2. Transmittance spectra from two common stains used in histopathology. (A) Spectra from the reaction product of peroxidase and aminoethylcarbazole (AEC) and hematoxylin. (B) Spectra from Gomori trichrome stain for connective tissue.

The wavelengths used to acquire images for subsequent ratiometric analysis were selected on the basis of the differential absorption of the stains used to render stained regions bright and counterstained regions dark in an intermediate gray valued white space. For example, using transmittance data (Figure 2A) for AEC-immunostained objects in Figure 1, AEC pixels illuminated with 3000 U of light had gray values of 0.18 x 3000 or ~540 at 500 nm and values of 0.40 x 3000 or ~1200 at 605 nm. The ratio of the 605-nm image to the 500-nm image multiplied by a scalar (3000) produced AEC pixels of ~6600. The hematoxylin-counterstained regions had values of 0.61 x 3000 or ~1830 at 500 nm, 0.40 x 3000 or 1200 at 605 nm, and values of 1200/1850 x 3000 or ~1970 in the ratio image. The unstained white space pixels had values of 3000 minus the background or ~2800. The actual intensity of a given pixel depended on transmittance which, in turn, depended on the amount of stain or thickness of reaction product projected into the pixel. Although the minimal detection levels had not been determined, it became clear that even the most faintly stained objects were easily detected. Hence, weakly to well-stained sections for routine pathology were more than adequate for analysis.

A ratio image was chosen to accentuate the spectral differences of the stains in question in an image format for subsequent threshold segmentation of the objects of interest. The ratio image avoided the problem of negative pixel values associated with a difference image and defined the mean intensity of unstained white space at a value near the ratio scalar. In practice, threshold levels determined by visual inspection were remarkably constant from image to image, slide to slide, and day to day. Segmentation of a stained object at an appropriate threshold did not depend on the amount of stain or counterstain but on whether or not stain was present in the image pixel. Hence, once a threshold was determined for a set of exposure conditions, subsequent images could be acquired and analyzed without operator intervention during an analysis session.

Selecting a threshold value to segment objects of interest can be a subjective process that can vary from image to image during an analysis session. In the ratio image, threshold determination is simplified because the objects of interest are, by design, at values greater than or less than the white space surround. Using the mean white space intensity, ~2800, and standard deviation, ~250, determined from the ratio image of a blank slide, a threshold value equal to the mean plus two standard deviations was used to segment bright objects. Similarly, for dark objects, a threshold equal to the average white space intensity minus two standard deviations was used. Although this method was subjective, in that visual inspection was the final criteria for accepting the threshold value, it provided consistent segmentation of objects of interest during an image analysis session. More sophisticated automated threshold procedures (Weaver and Au 1997 ) requiring no operator intervention are now under study.

Application of Spectral Ratio Image Analysis
Immunohistochemical labeling has been a powerful tool in the study of cells and biochemical mechanisms associated with disease states. In cancer, angiogenesis in tumors is regarded as an important factor in tumor growth and malignancy. The degree of tumor vascularization, as measured by quantitation of immunolabeled angiogenic endothelial cells in tumor sections, is believed to be a key prognosticator of tumor development. Results of applying the spectral ratiometric method to angiogenesis measurements in a mouse–human xenograft tumor model are shown in Figure 3. For this example, angiogenic endothelial cells were immunohistochemically stained with endothelial cell-specific antibody and visualized with an AEC–peroxidase reaction product. Using spectral conditions for AEC and hematoxylin on the tumor image shown in Figure 3A, an image acquired at 605 nm was divided by an image acquired at 500 nm to produce the ratio image in Figure 3B. The AEC-positive microvessels were rendered bright, with intensities ranging from 3500 to 15,000 U, whereas the nuclei had intensities of 2400 or less. Using the average white space value and standard deviation to determine the thresholds for endothelial cells and nuclei as described above, both features were easily segmented to produce an image of AEC-stained endothelium (Figure 3C) and hematoxylin-stained nuclei (Figure 3D). Determination of endothelial area and nuclear area was easily done by counting the non-zero pixels in the respective images. The method proved to be reasonably fast, such that large studies of up to 100 slides with five measurement fields per slide could be performed in a 4-hr period.



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Figure 3. Ratiometric analysis of a section of Leydig cell tumor from a mouse animal model in which the endothelial cells of the microvasculature have been immunolabeled for pan-endothelial cell antigen with MECA-32 antibody and visualized by aminoethylcarbazole–peroxidase reaction product. (A) RGB image illustrating endothelial cells in Leydig cell tumor. (B) Ratio image of A obtained by dividing an image acquired at 605 nm by an image acquired at 500 nm. (C) Image containing segmented endothelial cells prepared by thresholding image B for all pixels at or above the mean white space intensity plus 500 U, i.e., 3500 U. (D) Image of segmented nuclei obtained by threshold image B for all pixels at or below the mean white space intensity minus 1000 U, i.e., 2000 U. Bar = 50 µm.

Figure 4. Ratiometric analysis of collagen deposition around fibers in a subcutaneous implant of a biomedical polymer. A An RGB image of a Gomori trichrome stain section of tissue surrounding an implanted fabric after 56 days. The polymer fibers appear as round white spaces, collagen is stained blue-green, and cell cytoplasm and nuclei are stained dark red. (B) Ratio image of field in A obtained by dividing an image acquired at 615 nm by an image acquired at 500 nm. The range of intensities in this image is 600–15,000 U. (C) Image of segmented regions containing aniline blue-stained collagen obtained by thresholding B at or above the white space mean intensity plus 500 U. (D) Image of segmented nuclei obtained by thresholding B at or below the white space mean intensity minus 500 U. Bar = 20 µm.

Extracellular collagen matrix production is an important indicator of the pathological state in a variety of diseases and of the healing response to a variety of tissue insults, including radiation therapy and biomedical implants. In wound healing and fibrotic disease states, in situ measurements of extracellular matrix density from tissue sections have been particularly important. In a study of tissue growth into a subcutaneously implanted biomedical fabric (manuscript in preparation) (Figure 4), matrix production in and around the fabric fibers had to be measured. For this, trichrome-stained sections for collagenous matrix tissue were imaged under conditions to segment the aniline blue-stained collagen. Based on Figure 2B, images at wavelengths of 615 and 500 nm were collected and ratioed to produced highlighted collagen matrix and dark counterstained nuclei, as shown in Figure 4B. The segmentation threshold for this image was set by the mean white space intensity plus or minus 300 U. This produced excellent segmentation of the collagen matrix and cell nuclei from which the volume density of collagen matrix was estimated as the number of non-zero pixels divided by the total number of pixels in the image. In addition, the segmented nuclei in Figure 4D were further analyzed on the basis of size to extract the number of foreign body giant cells per field of view. Results obtained by visually counting foreign body giant cells per high-power field and those obtained by size analysis of the nuclear image were in complete agreement (not shown).


  Discussion
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Summary
Introduction
Methods and Materials
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Literature Cited

Here we describe a new, rapid, and convenient method of color image analysis of use in histopathological studies. The method takes advantage of the unique absorption behavior of the dyes used to stain features of interest and image algebra with images acquired at specific wavelengths to produce a gray ratio image in which the features are highlighted with respect to a uniform unstained surround. The ratio image is then segmented by routine thresholding techniques for subsequent quantitation. The method is new in that it utilizes a new type of filter, a random access, tunable liquid crystal filter, which passes images at different wavelengths with no image distortion or displacement. The method is rapid in that it uses simple image algebra to calculate gray scale ratio images such that the acquisition and analysis of one field of view requires 20–30 sec. The method is convenient in that ratio wavelengths typically have to be determined only once for a given stain procedure and measurements can be made by anyone trained in recognizing the appropriate fields to be analyzed. Finally, this method should work for any stain combination and has been used for the following stain combinations: Safranin O/hematoxylin for proteoglycans in articular cartilage, Oil red O in adipose tissue, peroxidase–diaminobenzidine immunostain with either fast green or hematoxylin counterstain, alkaline phosphatase–AP–Red (Zymed Laboratories), and –Vector Red (Vector Laboratories) immunostain and counterstains in a variety of tissues.

Color image analysis to date has been largely limited to RGB and computed HSI images acquired through CCD cameras with either one or three sensor chips. Although the exact wavelength ranges for most of these is proprietary information, nominal wavelengths of 635, 535, and 480 nm, for red, green, and blue images can be assumed because these match the response of the human eye. Segmentation of stained objects has been achieved in some cases by simply thresholding on one of the colored images or by defining more complicated algorithms that use all three RGB intensities (Willemse et al. 1994 ; Ruifrok 1997 ), HSI intensities (Em et al. 1996 ), or all six RGB and HSI intensities (Smolle 1996 ). In each of these methods, a gray image whose pixels intensities are determined from linear combinations of RGB or HSI images is segmented to isolate the colored objects. Similar procedures are used in commercial image analysis softwares that use a "magic wand" tool and operator intervention to select an object and all similar objects in the given image. The need to use up to six parameters most likely stems from the limited spectral content of the RGB image.

Related work employing multiple wavelength analysis obtained with a conventional microscope and digital camera has focused on the amount of stain present in an object of interest. Using wavelengths corresponding to the non-overlapping absorption bands of a stain–counterstain combination, Bacus et al. 1989 have provided insight into the biochemical composition of cancerous cells and tissues. Zhou et al. 1996 have designed a multiple wavelength algorithm using RGB wavelengths to estimate the amount of counterstain and peroxidase immunolabel reaction product in an attempt to quantify protein antigens and biochemical content of stained structures. The assumption in these methods is that absorption and concentration obey the Lambert–Beer relationship and that the amount of stain, once determined, can be accurately related back to antigen concentration in the tissue. Although studies are currently under way with the present system to further these methods, it was realized early on that a rapid, fundamentally accurate, and easy to use system for morphometric analysis of stained tissue sections was needed. The system and method described here fulfill that need.

Received for publication January 11, 1999; accepted May 11, 1999.
  Literature Cited
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Summary
Introduction
Methods and Materials
Results
Discussion
Literature Cited

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