SigmaScan Product Uses

 

The following whitepapers highlight some of the many application areas leveraging SigmaScan Pro:

 

Agar Plate Colony Counting and Size Determination


Detailed use of overlay planes in SigmaScan Pro produces a count and size distribution of bacterial colonies on an agar plate. The objective is to use image analysis to count and determine the size of bacterial colonies found on an agar plate. SigmaScan Pro is used to automate colony counting and measurement.

The procedure described here, or modifications of it, can be used for simple colony counting or more complex analyses of colony data. It has application in the areas of immunology, bacteriology and microbiology.

Capture Image
The agar plate image was captured using an inexpensive solid-state CCD camera and a standard off-the-shelf frame grabber board. The agar plate at left; was back lit using a standard light box.

 

 

 

Spatially Calibrate the Image
The image was spatially calibrated using the Image, Calibrate, Distance and Area menu option. A 2-Point Rescaling calibration was performed using millimeter units. Colony areas will then be reported in square millimeters. Uneven lighting over the image was corrected by using a pseudo-clearfield operation. This was done by selecting Clearfield, Generate Pseudo-Clearfield from the Image menu. The pseudo-clearfield was calculated using a rectangular grid with 8 x and y divisions. The image shows pseudo-clearfield that is used to correct for the uneven illumination of the plate. The plate edge is visible in the clearfield image. This is an artifact of the “pseudo” correction process that for this application won’t affect the measurements. It would cause errors if intensity measurements were being made.

Perform Equalization to Correct the Original Image
Select Clearfield, Perform Equalization… from the Image menu to correct the original image. The result is shown at left.

 

 

 

Increase the Contrast Between Colonies

Increasing the contrast between colonies and the surrounding region will help identify the colonies by thresholding. Image contrast is improved by performing a Histogram Stretch from the Image, Intensity menu. This procedure measures the gray levels in the image. The user then “stretches” the range of gray levels with significant magnitude over the entire 255 level intensity range. In this case moving the Old Start line with the mouse to an intensity of 64 will eliminate the effect of the insignificant dark gray levels and improve the contrast. The results are shown at left.

 

Identify by Thresholding

The colonies can be identified by thresholding the intensity level to fill in the darkest objects. This is done by selecting Threshold, Intensity Threshold from the Image menu. A dark gray intensity range of about 60 to 160 was used to identify the objects shown in the green overlay plane shown in the image at left.

 

 

Identify Include Portions

The objects that are identified include portions of the edge of the plate. These objects can be removed by using image overlay layer math to intersect the bacterial colony objects with an overlay plane consisting of the interior of the agar plate. We will create the latter overlay by filling light pixels in the interior of the plate. Move your cursor radially from the interior of the plate toward the edge and observe the pixel intensity values. As you approach the plate edge the intensity decreases in the darker pixels. In the image at left, the plate edge has an approximate intensity of 180.

Select the Fill tab In the Measurement, Settings… dialog and select the Manual Threshold method. Set the Level to be 180 and the option to select objects that are lighter than this level. Select the Fill Measurement mode (paint bucket icon) and left click in the interior of the plate to fill it. You may need to set the source overlay to red in the Measurements, Settings, Overlays dialog. There are “holes” in the red overlay plane that were not filled since they contain dark pixels from the bacterial colonies. To fill them select Image, Overlay Filters and select the Fill Holes option. Let both the source and destination overlays be red. The resulting image is shown at left. The red circular overlay plane contains the green bacterial colonies.

Identify the Intersections of Red and Green Overlay Planes

The overlay math feature is used to identify the intersection of the red and green overlay planes. From the Image menu select Overlay Math and specify red and green to be the source layers and blue to be the destination layer. Then ADD the two layers to obtain the intersection. The resulting image is shown at left.


Color Measurement of Leaf Disease States


Setting color threshold ranges from a color intensity histogram in SigmaScan Pro allows separation and measurement of diseased areas from normal areas of the leaf.
Capture Image – Create Bitmap File

Two leaves were imaged using SigmaScan Pro, a COHU Color CCD camera and the True Vision Targa Plus 16/32 frame grabber board. A 24-bit color bitmap file was created at left. The image contains almost all colors but red, brown and yellow predominate. The image was calibrated using a 2 point calibration in the Calibrate Distance and Area dialog and the known width of the left leaf. A color threshold was performed using the Color Threshold dialog from the Image, Threshold menu.

Select Threshold Range for Red

The goal here is to separate and measure the area of the leaves that are red and brown-yellow. To do this we will threshold these two ranges of the intensity histogram. The [0 to 53] threshold for the red colors as shown at left.

 

 

Select Threshold Range for Brown-Yellow

The threshold range for brown-yellow is shown here at left.

 

 

 

Apply Threshold Ranges

Applying these two threshold ranges to the leaves results in leaf areas covered by the red and yellow overlay layers for the red and brown-yellow leaf colors. These are shown at left, respectively. Note the lack of spatial overlap of the two overlay layers.

 

 

 

Results

The areas of the leaf objects defined by color thresholding were computed using Measure Objects from the Measurements menu. These values were placed in columns 1 and 2 of the worksheet for the red and brown-yellow leaf colors, respectively. Selecting the Statistics worksheet tab computes various descriptive statistics.

The total leaf areas were computed from the mean value and number of values. There were 727 red leaf objects detected with at total area of 65.4 square centimeters. In the brown-yellow leaf regions, 1127 objects were found with a total area of 78.9 square centimeters. These results are shown at left.


Coral Coverage and Diversity Investigations


Preliminary investigations of coral coverage and diversity measurements were conducted in the shallow reef zone of a fringing reef in central Quintana Roo, Mexico. Using digitized images from photo transects of the reef, substrate coverage and diversity determinations for hermatypic coral species were conducted using SigmaScan Pro.

Results indicated low substrate coverage (9.9%) and diversity measurements (0.94) for all hermatypic species within the shallow reef zone. SigmaScan Pro spatial measurements of coral species from photo transect images were used to quantify total coral coverage and diversity.

Project Methodology

Prior to using SigmaScan Pro, each photo transect image was processed as slide film, projected at a size ratio of 1:1 (according to a size standard within each image) and superimposed onto a grid. The image superimposed on each grid-point was then identified and recorded. All diversity and substrate coverage measurements were determined using this method.

Using SigmaScan Pro, it is now possible to accurately measure substrate coverage and coral diversity as a function of true substrate coverage as shown at left. The photo-transect image shows a total area of 2,319 cm2. Colonies 1-4 represent one species and cover 3.9, 6.2, 832, and 637 cm2 respectively. Colony 5 is a separate species and covers 114 cm2. Total hermatypic coral coverage in this image is 1,594 cm2 or 68%.

Project Measurement Results
This image is an excellent example of how SigmaScan Pro can be used to easily retrieve real spatial data which previously would have required a great deal of time and effort. A size standard is used to calibrate measurements of coral colony area or substrate coverage. The Area measurement feature is used to measure the calibrated areas for each of the coral colonies (#1-5).

A more thorough investigation of coral coverage, diversity and coral pigmentation as a response to light availability will be conducted on the deeper fore-reef region. These images will be analyzed with SigmaScan Pro for accurate measurements of total coral coverage by species and total coral diversity (indexed to total substrate coverage per species).

However, the next step will be to take advantage of the software’s additional color capability and perform analyses on the intra-specific coral pigmentation variability as a response to light availability. This will be accomplished using an RGB color standard to color correct each image to true values during digitization. Coral pigmentation changes in response to light availability will be determined by analyzing the mean values within each RGB color channel for each coral colony and noting if any correlations exist.

Coral coverage and diversity measurements are important indicators of reef health, and the very nature of image digitization lends itself to data archival for comparative studies as a function of time. Coral coverage and diversity measurements have long been held as mainstays of coral reef monitoring projects. An easy and successful means of retrieving comparative coral pigmentation data will aid future research projects in monitoring several threats to reef health which manifest themselves in coral color changes (e.g. coral bleaching, “black band” disease, etc.). We plan to use SigmaScan Pro to develop this methodology. – Scott P. Milroy, Texas A&M University Corpus Christi


Measurement of Oil Droplet Size and Distribution


SigmaScan Pro and TableCurve 2D are used to characterize the size distribution of oil droplets.

SigmaScan and TableCurve 2D Working in Tandem
SigmaScan measured the oil droplet radius and TableCurve found the Weibull function to best characterize their size distribution. Oil droplets, suspended in a fluid column were imaged using conventional CCD camera technology and a PC compatible frame grabber board. The image is shown at left.

 

Enhancing Image Contrast

The image was calibrated using a two point calibration from the Calibrate, Distance and Area option in the Image menu. The contrast was enhanced using the Histogram Stretch procedure (Image, Intensity menu) so that operators could better visualize the oil droplets (the Old Start line end point was dragged with the mouse to intensity 192 which stretches the light gray (192) to white (255) range over then entire 0 – 255 range). The enhanced image is shown at left.

 

Using Intensity Thresholding
prd_oil3Intensity Thresholding the image in the intensity range 0 – 140 using the Image, Threshold option selected the darker oil droplets. The selected oil droplets are shown in the red overlay plane in the image at left.

 

 

Using the Fill Holes Feature
Due to the surface reflections, intensity thresholding will not select all pixels in some of the oil drops. The Fill Holes feature in the image, Overlay Filters dialog was used to allow accurate droplet area measurements to be made. This fills the holes in the droplets as shown at left.

 

 

Generate File Report

The objects on the red overlay plane were then counted and the parameters perimeter, area, ferret diameter, shape factor, compactness and number of pixels measured using the Measure Objects option in the Measurements menu. These measurements were selected from the list in the Measurements tab in the Measurements Settings dialog. A macro was written to compute the circular radius of each droplet using the equation R = (A/pi)^0.5 and the results placed into the worksheet. A histogram of droplet radius from 0 to 10 microns was also computed. An ASCII file report was generated and formatted in Excel.

Graph Results
The histogram data in the last two columns was copied into TableCurve 2D. All peak functions were selected from the Custom Equation dialog and the Weibull distribution found to fit the data best. The TableCurve graph of these results is shown at left.- Colin T. Sim, Labtronics, Inc


Meat Marbling


Use SigmaScan Pro to determine the area percentage and spatial fat distribution in meat.

This application shows how SigmaScan Pro may be used to determine the area percentage and spatial distribution of fat in a cut of meat.

Acquire Image and Calibrate
Fat areas and x,y center of gravity positions are measured and an interesting application of overlay layer mathematics is shown. A pork chop was imaged using a commercially available frame grabber and a black and white solid-state CCD camera. A two-point calibration is used across the 5 inch width of the pork chop (Image, Calibrate, Distance and Area) to obtain measurements in inches units. The outline of the meat section is necessary since a quantitative measurement of the percentage of fat is required.

Set Options in Measurements and Select Trace Measurement Mode
Set up the options in Measurements, Settings… to outline the pork chop and make subsequent measurements of fat center of mass positions and area. Select CM Binary X, CM Binary Y and Area in the Measurements tab to be placed in columns A, B and C of the worksheet. Select Continuous Streaming in the Trace tab. From the Overlays tab select the Source Overlay to be yellow. To trace the meat outline select Trace Measurement Mode from the Mode menu (or click the Trace Mode icon in the Measurement Toolbar). Press the left mouse button and trace the meat outline. When you have nearly completed the tracing, right click to connect the last point to the first. The outline will be filled and the area computed. This is shown with a yellow overlay at left.

Using Intensity Thresholding
Uncheck the yellow overlay from the View menu to display the original image. An Intensity Threshold of the original image is now performed over the range [190-255] to show the fatty area (Select Image, Threshold, Intensity Threshold). This is shown as the red overlay plane at left.

 

Using the Overlay Math Feature
Overexposed areas outside the meat can be excluded by intersecting the yellow and red overlay planes. Use the Overlay Math feature in the Image menu to do this. Select Source 1 and Source 2 to be the red and yellow overlay planes and the Destination overlay plane to be blue. Selecting the And operation intersects these two overlay planes and produces the fat objects shown at left.

 

Identify Fatty Areas
Select Measurements, Measure Objects to measure all fat objects. The x,y locations of the centers of mass and areas of all objects are placed into the worksheet. One objective was to compute the fat percentage with and without the subcutaneous fat shown at the bottom of the chop. To exclude the subcutaneous fat, blue overlay pixels were erased to disconnect the subcutaneous fat where it touches the upper meat region at the lower right portion of the image.

To do this select Measurements/Settings/Overlays and change the Source Overlay to blue. Next, select Mode/Overlay Draw Mode and hold the right mouse button down to erase blue overlay pixels. Measure all fat objects again but reselect the columns in the worksheet to be D, E and F (Measurements, Settings, Measurements tab, Column). The x,y locations of the centers of mass and areas of all objects are placed into the worksheet. Now right click on the subcutaneous fat object and delete it. The worksheet row for this object is also deleted. The identified fatty areas minus the subcutaneous fat region are shown at left.

View the Results
The resulting worksheet is shown at left. The first three and second three columns are the measurements of the blue overlay regions from figures 4 and 5 respectively. A macro was then written to compute the total and percentage areas with and without the subcutaneous fat region. These results are shown in column H. As expected the percentage of fat without the subcutaneous region is dramatically reduced. The View, Graph feature and the x,y center of mass measurements were used to display the spatial distribution of the fat objects.


 

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