Fact Sheet | Version 4.0 | 081604
SEG MENTATION
IMAGE SEGMENTATION is required for obtaining useful and reliable measurements from images, and is considered the most fundamental step in image analysis applications. It refers to distinguishing different objects
(phases) in the image both from the image background and from each other, and is as such the basis of subsequent measurements.
The segmentation capabilities can be used on any type of images, for measuring intensity information, absolute as well as relative. Segmentation and
measurement capabilities can be used in conjunction with e.g. motorized stages, and time-lapse acquisition.
A few small steps in VIS
VIS is capable of dealing effectively with segmentation of the complex and varied images encountered in
practical life-science research. Measurements are obtained based on the segmented image. Typical applications are:
– Counting: Cells, colonies, particles and other
isolated objects.
– Morphometry: Measurement of area,
circumference, and other shape related parameters
– Training is implemented as simple “teach by examples”.
– Expert knowledge about the application can be incorporated as pre- or post processing steps, making results more robust and immediately useful.
– All types of measurement variables can be defined, and
data management is fully integrated in the workflow.
– All necessary information is stored in application specific segmentation configurations.
of objects in the image.
– Intensity: Quantification of intensity, density,
and related parameters within identified regions.
– Texture: Quantitative descriptors of textural
properties within identified regions.
Training of the system
VIS is segmenting entire images into different tissue/
object types, based on a few examples defined by the
Definition of three “tissue” types,
healthy tissue, dead tissue and background.
Every single pixel in the image is automatically assigned to the tissue type
it resembles the most.
Figure 1 Teaching by example. Turquoise: healthy tissue. Blue: dead tissue.
Green: background. Quantitative measurements such as areas or object
counts are immediately available.
user, as illustrated in Figure 1.
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Fact Sheet | Version 4.0 | 081604
SEG MENTATION
Much more than traditional phase-analysis
Incorporating expert knowledge
Phase analysis is the most common implementation
Relevant objects in images are not always well sepa-
of image segmentation, defining a phase as an intensity
rated by color. In such cases, more specific know-
interval in one or more spectral bands. This approach
ledge can be used for image segmentation or identi-
has obvious limitations in much practical work.
fication.
Modern statistical classifiers combined with pre- and
VIS implements a number of filters capable of enhanc-
post-processing, enables the VIS user to approach a
ing specific local image properties, such as blobs (e.g.
very broad class of practical problems.
cells or particles) or lines (e.g. vessels or membranes).
In the example shown below, traditional phase-analy-
These filters can be used for creating robust segmen-
sis is unable to separate red cells and brown cells (mid-
tation schemes.
dle). Using an advanced classifier, the desired result is
In Figure 3, the profiles of the chondrocytes are (almost)
obtained (right).
round. The ”Blob filter” strongly enhances areas with
high probability of cell presence, which is shown in
Figure 4.
The Blob filter is part of the comprehensive library of
image filters for enhancing structures of interest, or
suppressing noise and other unwanted phenomena
such as skewed illumination.
Figure 2 Segmentation of red and brown stained cells from Pancreas tissue. Left: original, Middle: traditional phase analysis and Right: advanced classifier. In the segmentation results, red cells have color code blue,
brown cells are colored green and pixels identified as background is
yellow.
Output from these filters can be included in the image
segmentation as extra feature images, adding to the
spectral information (e.g. red, green, blue).
Any number of feature images can be generated and
used as input to the multivariate image classifiers implemented in VIS.
Figure 3 H and E stained joint tissue. The objective is to count the number of blue, rounded cell nuclei. Other objects, such as the larger areas in
the bottom left of the image, have the same color as the nuclei, but
should not be counted.
Figure 4 Small rounded objects respond strongly to the “Blob filter”. Note
how the larger blue areas in the bottom left respond weakly to the Blob
filter.
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Lung washout with differently sized cells.
Intermediate result of classification with linear
Bayesian classifier.
A series of post-processing steps has cleaned up
the result leaving only objects of interest.
Refining results
In VIS, knowledge about the application is easily con-
ment variables can be defined for obtaining a wide
verted into post-processing steps, turning image seg-
range of morphometric measurements.
mentation into useful results. Several post-processing
With many studies and large volumes of images, good
steps are available, allowing for modifications of inter-
data management is critically important.
mediate results based on e.g. size, shape, or context.
In VIS, the Segmentation module is integrated in the
The three images above show the result of a segmen-
database, making good data management effortless
tation followed by post-processing.
and fully integrated with the workflow.
Measurement variables are automatically stored in
Output and Data Management
the database, where they remain associated with the
pertinent image.
The segmentation provides an overlay image which is
The measurement variables can be viewed and ex-
an abstract representation of all objects or ”phases” in
ported from the Excel view provided in VIS, as shown
the image. By changing the transparency of this over-
below.
lay, and view the original image, the user can inspect
the quality of the segmentation result at all times.
Based on the abstract representation, quantitative
measurement variables can be defined for measuring
e.g. number of objects, area of objects, or intensity of
objects. Furthermore, various functions of measure-
Figure 5 View of measurement variables in the database.
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Counting and identification
Counting and identification is probably the most com-
Example output:
mon and time-consuming type of image assessment
in the life-sciences.
VIS offers several effective features for accomplishing
various counting and identification tasks.
An example is the counting of cell profiles in brain tissue sections, as shown below. With VIS it is almost effortless to obtain measurements such as, total number of dark/bright cells, as well as cell densities.
In parasitology, it is of interest to identify specific parasites. In the example below, the task is to find and
count the number of eggs of Paragonimus westermani, if present.
Figure 7 Original image.
Figure 8 Identification of two eggs of Paragonimus westermani. The
identified eggs have been marked white.
Figure 6 Cell counting in brain tissue. Top: original image. Bottom: Segmentation result. Dark cells are labeled blue, bright cells are labeled pink.
To obtain the above results, knowledge about the objects color and size has been used, and incorporated
into the segmentation configuration.
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Technical Specifications
Teaching by example
Post-processing (refinement)
Freehand painting
Fill holes
Region growing
Change object type based on
Pre-processing
Basic processing
Abs, Negate, Not, Add
Add, Subtract, Multiply, Divide
Square, Square root, Ln, Exp
Local filters
Unconditionally
Area
Circularity
Neighboring objects
Erode
Dilate
Convert objects to and from image masks
Mask change
Mean, Median, Modus, Standard deviation
Polynomial based filters
Smoothing
Gradient
Orientation
Laplace
Linear structures
Blob structures
Unsharp masking
Mean
Median
Scaling
Color transformations
RGB
IHS
Chromaticity, Contrast
Quantitative measurement variables
Based on segmented image
Object area
Object circumference
Number of objects
Image intensity statistics inside segmented objects
Minimum
Maximum
Mean
Standard deviation
Median
Modus
Entropy
Quick segmentation
Multivariate statistical classification
K-means clustering
Configuration load/save
Batch processing of large image sets
Fuzzy K-means clustering
Linear Bayesian classification
Quadratic Bayesian classification
Phase Analysis
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Fact Sheet | Version 4.0 | 081604
Application screen shot
Above, the segmentation module was used for measuring body composition based on CT-scans performed 5 mm below the proximal ends of the pelvic
bones in male SD rats, using a Stratec XCT Research AS (Stratec Medizintechnik Gmbh. The image was automatically segmented into air, lean tissue, bone,
and fat. The area and density of each segment (phase) was measured and results automatically stored in the system database. This module significantly
reduces manual labor associated with image analysis, while improving precision of the measurements. The illustration is kindly provided by Dr. Christian
Fledelius of Novo Nordisk, Site Måløv, Denmark.
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