Newer Image Segmentation Methods for Biomedical Object Detection
Tarundeep Singh Dhot Dept of ECE
Concordia University Montreal, QC H3G 1M8
ABSTRACT The basic aim of the study is intended towards
the field of image segmentation of cells with the
aim that it can be further extended to detect cell
inclusions. The study can be divided into three
phases. The first phase deals with the
segmentation of individual cell from a cell
image. Three segmentation approaches have
been used for this purpose. This phase is
followed by image mapping of the cell into
various image intensity plots. It is then intended
to use a Genetic Algorithm to locate the global
optima of the plot.
Categories and Subject Descriptors I.4 [Image Processing and Computer
Vision]: Segmentation – pixel classification
General Terms Algorithms, Experimentation
Keywords
Image Segmentation, Genetic Algorithms
1. INTRODUCTION The field of Biomedical Image Processing is an
exciting and growing cross-disciplinary field.
An exponential increase in the power of digital
processors, a steady growth in the field of
Digital Image Processing principles has given a
much needed impetus for emerging as a mature
subject in its own rights. One of the most
upcoming and active areas of interest is the
analysis of cell images for detection of cell
inclusions. Cell image segmentation is a
necessary first step of many automated
Biomedical Image Processing procedures.
Image segmentation is the identification and
isolation of an image into regions that one
hopes, correspond to structural units. It is an
essentially important operation in biomedical
image processing since it is used to isolate
physiological and biological structures of
interest. General approaches to segmentation
can be grouped into three classes: pixel-based
methods, regional methods and edge-based
methods.
Traditionally, pixel-based methods are the
easiest to understand and to implement, but are
also the least powerful and, since they operate
on one element at a time, they are particularly
susceptible to noise. Continuity-based and edge-
based methods approach the segmentation
problem from opposing sides: edge-based
methods search for differences while continuity-
based methods search for similarities.
In this study, three segmentation approaches
have been used: pixel-based methods,
continuity-based multi-thresholding and the
third one is based on a recent research
publication “Automatic segmentation of Cells
from Microscopic Imagery using Ellipse
Detection”. It is also important to mention the
use of morphological operators in the last
approach. Morphological operations have to do
with processing shapes. In this sense they are
continuity-based techniques, but in some
applications they also operate on edges, making
them useful in edge-based approaches as well.
After loading the image, pre-processing of the
image was carried out in terms of reduction of
noise using various filters.
Once the image is pre-processed and
successfully segmented, mapping of the image
of the individual cell is done into various image
intensity plots. These intensity plots of the
image represent the segmented image into
various 3-Dimensional surface plots of their
matrix data. These intensity plots show exactly
the location of the cellular inclusion on the basis
of presence of a region (inclusion) having
similar pixel values. Thus, the concentration of
the pixel values is represented in the intensity
plot over the entire cellular region. Once the
segmented image is represented in as a 3-D
intensity plot, the last intended step is to use a
genetic algorithm to locate the global optima.
This phase is currently in the process of
formulation.
The project is developed on the MATLAB 7.0
platform.
2. METHODOLOGY
As stated above, the study has three phases:
1. Segmentation of individual cells from cell
image;
2. Mapping image of individual cell to an
intensity image; and
3. Use of GA to locate the global maxima
Let us start of with cell segmentation.
In segmentation, the inputs are images but the
outputs are attributes extracted from those
images i.e. segmentation subdivides an image
into its constituent regions or objects. In the
study, three approaches to cell segmentation are
used. We shall see them individually in detail.
The first step in cell segmentation is loading the
image and converting it to gray scale from the
usual RGB color format. The image is then
further pre-processed. This step is common to
all the methods used. This step can be referred
to as the Loading Subroutine.
Loading Subroutine:
a) Load the image
b) Perform grayscaling.
c) Improve contrast for the grayscaled
image.
d) Improve precision (of the image from
step c).
It may be remembered that the images are all in
the bitmap format (.bmp). One inclusion from
the slide was pasted on a black background.
This also reduces the effect of noise. All
segmentation techniques are implemented on
this image to show variance in effectiveness of
the three methods.
2.1 Approach 1: Pixel-Based
Segmentation Using Minimal
Variance Iterative Technique For
Thresholding
The most straightforward and common of the
pixel-based methods is thresholding in which all
pixels having intensity values above, or below,
some level are classified as part of the segment.
Thresholding is an integral part of converting an
intensity image to
a binary image. Thresholding is usually quite
fast and can be done in real time allowing for
interactive setting of the threshold. The basic
concept of thresholding can be extended to
include both upper and lower boundaries
(slicing).
A major concern in pixel-based methods is
setting the thresholding or slicing level(s)
appropriately. Usually these levels are set by the
program, although in some situations they can
be set interactively by the user. Finding an
appropriate threshold level can be aided by a
plot of pixel intensity distribution over the
whole image, regardless of whether you adjust
the pixel level interactively or automatically.
Such a plot is termed as the Intensity
Histogram. Intensity histograms can be very
helpful in selecting threshold levels, not only
for the original image, but for images produced
by various segmentation algorithms. Initially
histograms can be useful in evaluating the
efficacy of different processing schemes: as the
separation between structures improves,
histogram peaks should become more
distinctive. If the intensity histogram is, or can
be assumed as bimodal (or multi-modal), a
common strategy is to search for low points, or
minima, in the histogram.
Keeping this in mind, there are two approaches
based on which Approach 1 is designed and
implemented.
The first approach is to improve the
determination of histogram minima is based on
the observation that many boundary points carry
values intermediate to either side of the
boundary. These intermediate values will be
associated with the region between the actual
boundary values and may mask the optimal
threshold value. However, these intermediate
points have the highest gradient, and it should
be possible to identify them using a gradient-
sensitive filter, such as the Sobel or Canny
filter. After these boundary points are identified,
they can be eliminated from the image, and a
new histogram is computed with a distribution
that is possibly more definitive. Thus, this leads
to slightly better segmentation of the cell.
The second threshold strategy is one that does
not use the histogram is based on the concept of
minimizing the variance between presumed
foreground and the background elements.
Although the approach assumes two different
gray levels, it works well even when the
distribution is not bimodal. The approach uses
an iterative process to find a threshold that
minimizes the variance between the intensity
values on either side of the threshold level
(Outso’s method). This can be referred to as
Minimal Variance Iterative Technique.
The above two approaches are thus incorporated
and implemented for Approach 1 as follows:
1. Load the image of the cell and perform the
necessary pre-processing like gray scaling
on the image (Loading Subroutine)
2. Display images after pre-processing along
with the intensity histogram
3. Remove the edge pixels from the image and
display the histogram of this modified
image.
4. Determine thresholds using minimal
variance iterative technique described
above
5. Apply this approach to threshold both
images.
6. Display the resultant thresholded images.
7. Perform class conversion of the final
segmented image
8. Plot the image intensity plots
To remove the edge boundaries, first
identify these boundaries using an edge
detection scheme. While any of the edge
detection filters can be used, for our approach,
Canny filter is used as it is more robust to noise.
The implementation of this filter will produce a
binary image of the boundaries. This boundary
image is converted to a boundary mask by
inverting the image. After inversion, the edge
pixels will be zero while all other pixels will be
one. Multiplying the original image by the
boundary mask will produce an image in which
the boundary points are removed (i.e. set to zero
or black). Perform class conversion (to double -
double-precision floating-point number array)
on the final segmented image in order to plot
the intensity graphs.
Figure 1.1 displays broadly the various image
transformations of Pixel-based segmentation
approach used for Approach 1. The original
image as shown in Fig 1.1 a) is first converted
to an increased contrast, high precision gray
scale image as shown in Fig 1.1 b). Firstly, edge
pixels are removed and then the image is
thresholded to give the final segmented image
shown in Fig 1.1 c). Intensity plot are
determined on Fig 1.1 c) which gives rise to the
intensity plot as shown in Fig 1.1 d) which is a
meshed image intensity plot.
2.2 Approach 2: Image Segmentation
Using Multi-Thresholding Technique
(Using AND - OR Operators)
The results of several different segmentation
approaches can be combined either by adding
the images together or more commonly, by first
thresholding images into separate binary images
and then combining them using logical
operations. Either the AND or OR operator
would be used depending on the characteristics
of each segmentation procedure. If each
procedure identified all of the segments, but
also included non-desired areas, the AND
operator could be used to reduce the artifacts.
Alternatively, if each procedure identified some
portion of the segment(s), then the OR operator
could be used to combine the various portions.
This approach is used in Approach 2 where first
two, then three, thresholded images are
combined to improve segment identification. The structure of interest is a cell which is shown
on a gray background. Threshold levels above
and below the gray background are combined
(after one is inverted) to provide improved
isolation. Including a third binary image
obtained by thresholding a texture image further
improves the identification.
Implementation of Approach 2:
1. Load the image of the cell and perform the
necessary pre-processing like gray scaling
on the image (Loading Subroutine)
2. Display images after pre-processing
3. Perform linear filtering of the image using
Low Pass Filter
4. Threshold the image its complement
5. Threshold texture image
Fig 1.1 a) Original Image
Fig 1.1 b) High Precision Gray
Scaled Image
Fig 1.1 c) Segmented Image (Thresholded
Edge Removed)
Fig 1.1 d) Image Intensity Plot (Mesh)
Figure 1.1: Image transformations using
Pixel –Based Segmentation
6. Combine the thresholded image and its
complement
7. Combine the above image with the
threshold texture image
8. Display thresholded and combined images
9. Perform class conversion of the final
segmented image
10. Plot the image intensity plots
It is possible to isolate some portions of the cell
by thresholding above and below the
background level. After inversion of the
thresholded image, the images are combined
using a logical OR. Since the cell also shows
some textural features, a texture image is
constructed by taking the regional standard
deviation. After thresholding, this texture-based
image is also combined with the other two
images.
The figure on the left displays the main image
transformation of the cell using the Multi-
Thresholding technique used in Approach 2.
Again, here too, first the original image shown
in Fig 1.2 a) is pre-processed into an increased
contrast, high precision gray scale image shown
in Fig 1.2 b). The image shown in Fig 1.2 c) is
obtained after use of logical OR operator on the
thresholded and inverse thresholded image. Fig
1.2 d) shows the logical OR operation of image
obtained in Figure 1.2 c) with the textural
image.
Fig 1.2 e) and 1.2 f) show the intensity plots for
Fig 1.2 c) and d).
2.3 Approach 3: Automatic
Segmentation of Cells from
Microscopic Imagery using Ellipse
Detection:
In this approach, a two-phase algorithm that
combines iterative thresholding with ellipse
detection is used for potent cell segmentation
mechanism. In this approach, a combination of
Morphological Operators with adaptive
thresholding is used in order to simultaneously
eliminate noise and locate inclusion regions,
hence, computing the best possible local
threshold levels for the inclusion extraction.
The main reason why this approach of also
included in this study was due to the use of
morphological operators. As stated earlier,
Fig 1.2 a) Original Image
Fig 1.2 b) High Precision Gray
Scaled Image
Fig 1.2 c) Combined Thresholded Image
Fig 1.2 d) Combined Thresholded and
Textural Image
Fig 1.2 e) Intensity Plot
Fig 1.2 f) Intensity Plot
Figure 1.2: Image transformations using
Multi –Thresholding
morphological operations have to do with
processing shapes. The two most common
morphological operations are dilation and
erosion. In dilation the rich get richer and in
erosion the poor get poorer. Specifically, in
dilation the centre or active pixel is set to the
maximum of its
neighbors and in erosion it is set to the
minimum of its neighbors. Since these
operations are often performed on binary
images, dilation tends to expand edges, borders
or regions, while erosion tends to decrease or
even eliminate small regions. The two processes
can be done in tandem, over the same area.
Since erosion and dilation are nonlinear
operations, they are not invertible
transformations i.e. one followed by the other
will not generally result in the original image. If
erosion is followed by dilation, the operation is
termed as opening. If image is binary, this
combined operation will tend to remove small
objects without changing the shape and size of
large objects. Basically, the initial erosion tends
to reduce all objects, but some of the smaller
objects will disappear altogether. The
subsequent dilation will restore those objects
that were not eliminated by erosion. If the order
is order is reversed and dilation is performed
first followed by erosion, the combined process
is called closing. Closing connects objects that
are close to each other, tends to fill up small
holes, and smooths an object outline by filling
small gaps. As with the more fundamental
operations of dilation and erosion, the size of
objects removed by opening and closing
depends on the size and shape of the
neighborhood that is selected.
Implementation of Approach 3:
1. Load the image of the cell and perform the
necessary pre-processing like gray scaling
on the image (Loading Subroutine)
2. Display images after pre-processing
3. Perform linear filtering of the image using
Low Pass Filter
4. Application of Open Morphological
Operation:
Erode Dilate.
5. Global Thresholding.
6. Multi-Region Adaptive Thresholding.
7. Second application of Open Morphological
Operation
8. Display thresholded images
9. Perform class conversion of the final
segmented image
10. Plot the image intensity plots
3. RESULTS
The results for the study are described in the
form on the images obtained from the
experiment. Image output obtained from all the
three approaches are displayed in a sequential
flow chart format for easier understanding. The
study was carried out on an Intel Pentium 4,
3.20GHz, 512 MB DDR RAM. Experiments
were performed on MATLAB 7.0.
As shown in Fig 2 for Pixel-based edge
removed thresholding, after the initial pre-
processing of the images, using a Canny filter,
the edges are removed (Fig 2 g). This image is
then thresholded to give the final segmented
image (Fig 2 h). The intensity plot (mesh) of
this image is shown in Fig 2 (i).
Fig 2.1 deals with the various intensity plots
depending on the function against which the
image is plotted. Three such functions are
shown: mesh, ribbon and surf.
Also displayed are two pairs of histograms (Fig
2.2 and 2.3), the first one comparing a simple
gray scale image to a high precision image, and
the second one showing improved separability
when boundaries are eliminated.
Similarly, the output images of Approach 2 i.e.
Multi-Thresholding are displayed in Fig 3. One
thing to note here is that Fig 3 (e) shows the
Thresholded image. This image is then inversed
(complemented) which is shown in Fig 3 (f) and
these both images are then logically ORed to
produce the Combined Threshold Image of Fig
3 (g). This image is again ORed with a textural
image shown in Fig 3 (h) to give rise to
Combined Threshold Textural Image shown in
Fig 3 (i). Fig 3 (j) shows the intensity plot of
only image Fig 3.1 (g) while Fig 3 (k) is the
intensity plot of Fig 3 (j).
Fig 3.1 represents the various intensity plots.
Fig 4 represents the output images of Approach
3: Auto-segmentation of cells. Fig 4 (e) and (f)
represent the first operation of the
morphological operators: Erode followed by
Dilation i.e. opening. Fig 4 (g) represents image
of cell after first thresholding. Similarly, Fig 4
(h) and (i) represent the “second opening”
operation while Fig 4 (j) represents the second
thresholding on the cell.
Fig 4.1 show the intensity plots after each
opening operation. Fig 4.2 shows the various
intensity plots for the final thresholded image in
Fig 4 (j).
4. DISCUSSION
In this project, an effort was made to study three
different segmentation approaches even though
no broad comparison study was made. As it can
be seen, the output segmented images of all the
three methods are completely different. This
shows how segmentation results from different
approaches can yield to completely different
that pixel based methods are particularly
Fig 1.3a) Original Image
Fig 1.3b) High Precision Gray
Scaled image
Fig 1.3c) First Opening
Fig 1.3 d) First Thresholding
Fig 1.3 e) Second Opening
Fig 1.3 f) Final Thresholding
Fig 1.3 g) Intensity Plot
Fig 1.3: Image transformation using
Automatic Segmentation Approach
susceptible to noise, since they operate on one
element at a time. Filtering can reduce the effect
of noise, thus is used in all the three approaches.
Also with Multi-Thresholding, we can logically
AND or OR images according to our
requirement which can be really helpful. Use of
textured image also increases the options for
use. Lastly, morphological operators allow for
manipulation of maxima and minima of an
image. Also with intensity plots, visualization
of cell inclusions improves as it shows
magnitude of relative intensity to its location.
5. FIGURES
5.1 Pixel Based Methods
5.1.1 Phases in Image Transition
a) Original Image b) Gray Scaled c) Increased Contrast
d) High Precision
e) Gaussian Low
Pass Filterf) 3-D Convolution
Filter (Canny)
g) Edge Removed
h) Segmented Image:
Thresholded Edge
Removedi) Intensity Plot (Mesh)
Fig 2: Pixel-Based Segmentation Using Minimal Variance Iterative Technique For
Thresholding
5.1.2 Intensity Plots
a) 3-D Mesh Plot
b) 3-D Ribbon Graph of Matrix
c) 3-D Surface Plots
Fig 2.1 Various Plots of Image Intensity for Pixel Based Edge-Removed Thresholding
5.1.3 Histograms
Fig 2.2: Histograms of Gray Scaled Image and High Precision Image. There is
significant improvement in the histogram of High precision Image
Fig 2.3: Images of cell with (upper) and without (lower) intermediate boundaries
removed. The associated histograms (right side) show improved separability when
boundaries are eliminated
5.2 Multi-Thresholding
5.2.1 Phases In Image Transition
a) Original Image b) Gray Scaled c) Increased Contrast d) High Precision
e) Thresholded
Image
f) Complement
of Threshold
g) Combined Threshold Image
h) Textural Image
i) Combined Threshold
Textural Image
j) Intensity Plot: Combined Threshold Image
j) Intensity Plot: Combined Threshold Textural Image
Fig 3: Image Segmentation Using Multi-Thresholding Technique (Using AND
- OR Operators)
5.2.2 Intensity Plots
(a) Intensity Plots of Combined Thresholded and Threshold Textural Image
(b) Intensity Represented by Various Functions
Fig 3.1: Intensity Plots: Multi-thresholding Technique
5.3 Auto-Segmentation Of Cells
5.3.1 Phases In Image Transition
a) Original Image b) Gray Scaled c) Increased Contrast d) High Precision
j) Intensity Plot (Mesh and Ribbon)
Fig 4: Automatic Segmentation of Cells
e) Erode - I
f) Dilate - I
g) Threshold - Ih) Erode - IIi) Dilate - IIk) Threshold - II
5.3.2 Intensity Plots Of Morphological Operations
a) Mesh Plot After Erosion - I
c) Mesh Plot After Erosion - II
b) Mesh Plot After Dilation - I
d) Mesh Plot After Dilation - II
Fig 4.1 Intensity Plots After Opening Operations
The above figure shows various intensity plots after the use of “Opening Operation” before and after
thresholding of the image. The marked change in the plots can be seen. The plots before thresholding suffer
from presence of noise in the image. After thresholding, presence of noise seen is minimal.
5.3.3 Intensity Plots
(a) 3-D Mesh Plot of Image (b) 3-D Surface Plot of
Image
(c) 3-D Contour Plot of Image
Fig 4.2: Intensity Plots for Automatic Cell Segmentation Method
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