Download Bacteria Colony Counter using Wiener Filter

IJECT Vol. 2, Issue 4, Oct. - Dec. 2011
ISSN : 2230-7109 (Online) | ISSN : 2230-9543 (Print)
Bacteria Colony Counter using Wiener Filter
1
Meena Malik, 2Anil Garg
,Dept. of RBIEBT, Kharar, Mohali, Punjab, India
Dept. of, MMEC, Mullana, Ambala, Haryana, India
1
2,
Abstract
Numerating microbial growth often required in bacterial
pathogens. There are several methods for enumeration of
bacterial colonies. An increased area of focus in Microbiology
is the automation of counting methods. Several obstacles need
to be addressed for methods that count colonies present, for
example on Petri plates. These obstacles include: how to handle
confluent growth or growth of colonies that touch or overlap
other colonies, how to identify each colony as a unit in spite
of differing shapes, sizes, textures, colors, light intensities,
etc. This method is designed to provide a degree of accuracy
in counting that could be correlated to the counts that would
be obtained using a well-trained operator. This method is used
to overcome these obstacles are thresholding, segmentation,
Watersheding, edge detection and mathematical morphology
operators. This method provides high degree of accuracy. Edge
detection is a terminology in image processing and computer
vision particularly in the area of feature detection and extraction
to refer the algorithm which aims at identifying points in a
digital image at which the image brightness changes sharply or
more formally has discontinuities. Sobel, Robert’s, peewit and
canny are edge detection technology. Thresholding is technique
used to discard irrelevant data and keep only the important
segment of data which lie above threshold curve. In this paper
used mathematical morphology operator. It is a mathematical
theory and technique for analysis and processing of geometrical
structures, based on set theory. Image segmentation is the
process of dividing image according to its characteristics e.g.
colour and objects present in the image. Watershed algorithm
can generate over segmentation or under segmentation on
badly contrast images using distance Transformation and
Markers approaches. Automation should be helpful in many
microbiological studies and biomedical research.
Keywords
Bacteria, Bacterial Colony, Thresholding, Segmentation,
Morphology Operators, Watershed.
I. Introduction
A colony counter is used to count colonies of bacteria or other
microorganisms growing on an agar plate. Bacterial colony is
a group of bacteria growing on a plate that is derived from one
original starting cell.Bacterial colony counting process is usually
performed by well-trained technicians manually.
A. The Conventional Approach
There are several systems available for numerating microbial
growth. These methods may utilize both direct and indirect
methods of counting colonies. The traditional plate count
method is considered an indirect method, desirable due to
its low cost.
B. The Automated Approach
An increased area of focus in Microbiology is the automation
of counting methods. Several obstacles need to be addressed
for methods that count colonies present, for example on Petri
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plates. These obstacles include: how to handle confluent growth
or growth of colonies that touch or overlap other colonies, how
to identify each colony as a unit in spite of differing shapes,
sizes, textures, colors, light intensities, etc. This method is
designed to provide a degree of accuracy in counting that could
be correlated to the counts that would be obtained using a
well-trained operator.
C. The Purposed Approach
This is an Industrial oriented ongoing project. It has vast
applications in pathological labs/Medical labs and other
Industries. Counting of bacterial colonies is done in the
following manner: Bacteria are grown onto filter for 24 to 48
hours to check the contamination level of the sample. To count
these bacterial colonies microbiologist uses some dyes so that
bacterial colonies appear as colored spots and our problem
is to count the number of these bacterial colonies. Further in
an Industry thousands of such samples are formed per day
and colonies on each sample are counted manually, then
this becomes a time consuming hectic and error prone job.
Goal of the project is to develop software to save time with
accurate results and fast delivery to customers. This project
will be extended in such a manner that it will count the colonies
after 6 to 8 hours priori, saving a lot more time.
D. Bacteria
Bacteria are unicellular microorganisms. They are typically
a few micrometers long and have many shapes including
curved rods, spheres, rods, and spirals. The study of bacteria
is bacteriology, a branch of microbiology. A group or cluster of
bacteria derived from one common Bacterium. A bacterial cell
is a unit of bacterial colony. In colony of bacteria there can be
thousands to millions of cells can be present. The morphology
of the colony of bacteria can be seen with naked eyes without
the help of any gear. Whereas the bacterial cell can only be
seen under microscope.
II. Methodology
A. Image Capturing
Bacterial Colonies are grown onto filter for 16 to 24 hours.
Some colored dyes are spread over each filter so that bacterial
colonies appear as colored spots. Now this filter is kept on a
Petri plate. Background is made of black or white intensity so, it
becomes easier to separate the filter from its surroundings while
processing the image. Petri plate is kept in a box containing
a digital camera and light arrangement. Images are then
captured using this arrangement. The collected images are
digitized on a computer utilizing a image processing software
package that has programming capabilities. The digitized
picture is processed using the various procedures described
to separate and detect the colonies present.
B. Extracting Image Contents
Extraction of Bacteria present on petri dish is done using various
procedures. Extraction procedure consists of following steps.
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Each extraction step has its own significance. Given below is
the detailed description of each step.
• Conversion of Colored image (RGB) to HSV
• Gray Scaling of Image
1. RGB
The RGB color format can represent any standard color or
brightness using a combination of Red, Green and Blue
components. For efficiency, this is typically stored as 24-bit
number using 8-bits for each color component (0 to 255) so
that for example, White is made of 255 Red + 255 Green +
255 Blue
2. HSV
HSV stands for hue, saturation, & value, and is also often
called HSB (B for brightness). HSV (Hue, Saturation and
Value) defines a type of color space. It is similar to the
modern RGB and CMYK models. The HSV color space has three
components: hue, saturation and value. ‘Value’ is sometimes
substituted with ‘brightness’ and then it is known as HSB.
3. Why Converting Rgb To Hsv Required
When it comes to computer vision, RGB values will vary a lot
depending on strong or dim lighting conditions and shadows,
etc. In comparison, HSV is much better at handling lighting
differences, and it gives an easy to use color value. The
advantage of HSV is that it provides a way of selecting colors
that is more intuitive to most artists.
4. Gray Scaling Of Image
Grayscale digital image is an image in which the value
of each pixel is a single sample, that is, it carries only
intensity information. Images of this sort, also known as blackand-white, are composed exclusively of shades of gray, varying
from black at the weakest intensity to white at the strongest.
C. Applying Adaptive Filter For Noise Removal
Adaptive filters adapt or learn the characteristics of the signal.
Therefore the adaptive median filtering has been applied widely
as an advanced method compared with standard median
filtering. The Adaptive Median Filter performs spatial processing
to determine which pixels in an image have been affected by
impulse noise. The Adaptive Median Filter classifies pixels as
noise by comparing each pixel in the image to its surrounding
neighbor pixels. The size of the neighborhood is adjustable,
as well as the threshold for the comparison.
1. Purpose
• Remove impulse noise
• Smoothing of other noise
• Reduce distortion, like excessive thinning or thickening of
object boundaries.
2. Wiener Filter
The most important technique for removal of blur in images
due to linear motion or unfocussed optics is the Wiener filter.
From a signal processing standpoint, blurring due to linear
motion in a photograph is the result of poor sampling. The
Wiener filtering is a linear estimation of the original image. The
approach is based on a stochastic framework. The orthogonality
principle implies that the Wiener filter in Fourier domain can
be expressed as follows:
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International Journal of Electronics & Communication Technology
ISSN : 2230-7109 (Online) | ISSN : 2230-9543 (Print)
Where, Sxx(f1,f2), Sŋŋ(f1,f2) are respectively power spectra of the
original image and the additive noise, and H (f1,f2) is the blurring
filter. It is easy to see that the Wiener filter has two separate
parts, an inverse filtering part and a noise smoothing part. It
not only performs the de-convolution by inverse filtering (highpass filtering) but also removes the noise with a compression
operation (low-pass filtering).
D. Implementation
To implement the Wiener filter in practice estimation has to be
done for power spectra of the original image and the additive
noise. For white additive noise the power spectrum is equal
to the variance of the noise. To estimate the power spectrum
of the original image many methods can be used. A direct
estimate is the period gram estimate of the power spectrum
computed from the observation:
Where, Y (k,l) is the DFT of the observation.
The advantage of the estimate is that it can be implemented
very easily without worrying about the singularity of the
inverse filtering. Another estimate which leads to a cascade
implementation of the inverse filtering and the noise smoothing
is
Which, is a straightforward result of the fact Syy=Sŋŋ+Sxx
|H|2 the power spectrum Syy can be estimated directly from
the observation using the period gram estimate. This estimate
results in a cascade implementation of inverse filtering and
noise smoothing:
The disadvantage of this implementation is that when the
inverse filter is singular, one has to use the generalized inverse
filtering. People also suggest the power spectrum of the original
image can be estimated based on a model such as the 1/
fα model.
E. Thresholding
Thresholding is one of the method of image segmentation .This
method is based on a clip-level (or a threshold value) to turn a
gray-scale image into a binary image. & it is used extensively
in many image processing applications. Individual pixels in a
grayscale image are marked as ‘object’ pixels if their value
is greater than some threshold value and as ‘background’
pixels otherwise. Generally the object pixels are ‘black’ and
the background is ‘white’. After thresholding a binary image
is formed where an object pixel is given a value of ‘1’ while a
background pixel is given a value of ‘0’
1. Purpose Of Thresholding
• To discard irrelevant data and keep only the important
segments of data which lie above threshold curve.
• To classify pixels on the basis of colour or a local
property.
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ISSN : 2230-7109 (Online) | ISSN : 2230-9543 (Print)
• To provide a bi-level picture that distinguishes objects from
background.
• To represent the text and diagrams in binary formats.
F. Mathematical Morphology
Morphological image processing (or morphology) describes a
range of image processing techniques that deal with the shape
(or morphology) of features in an image. The technique was
originally developed by Mat heron and Serra Des Moines in
Paris in 1964.The language of mathematical morphology is set
theory. The mathematical morphology simplifies image data,
preserves essential shape characteristics and eliminates noise.
Morphological operations are based on simple expanding and
shrinking operations. The primary application of mathematical
morphology occurs in binary images, though it is also used in
grey scale images.Morphological operations are typically applied
to remove imperfections introduced during segmentation.
Mathematical morphology is a foundation of morphological
image processing, which consist of a set of operators that
transform images according to the above characterizations.
The morphological operator dilation acts like a local maximum
operator. Erosion acts like a local minimum operator.
G. Watershed
There is a need to split the colony to get correct colony counts.
To separate the connected colonies intensity are considered.
Intensity gradient image as a topological surface, thus
Watershed algorithm can be applied to divide the clustered
colonies in the image just as water fold in the topological surface.
Watershed is method of image processing segmentation. Think
of the grey-level image as a landscape. Let water rise from
the bottom of each valley (the water from each valley is given
its own label). As soon as the water from two valleys meets,
build a dam, or watershed. These watersheds will then define
the borders between different regions. Watershed can be
used directly on the image, on an edge enhanced image or
on a distance transformed image. A grey-level image may be
seen as a topographic relief, where the grey level of a pixel is
interpreted as its altitude in the relief. A drop of water falling on
a topographic relief flows along a path to finally reach a local
minimum. Intuitively, the watershed of a relief corresponds
to the limits of the adjacent catchment basins of the drops
of water. Watershed segmentation is a morphological based
method of image segmentation. The gradient magnitude of an
image is considered as a topographic surface for the watershed
transformation. Watershed lines can be found by different
ways. The complete division of the image through watershed
transformation relies mostly on a good estimation of image
gradients. The result of the watershed transform is de-graded
by the background noise and produces the over-segmentation.
Also, under segmentation is produced by low-contrast edges
generate small magnitude gradients, causing distinct regions
to be erroneously merged.
III. Future Scope
Image processing is very advanced and extensive field which
needs extensive research and hard work. Following are the
suggestions for future work.
1. This work can be extended to run on different types of
micro biological bodies in order to count and evaluate their
different parameters like shape, density etc.
2. Further in this work regression analysis can also be done
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IJECT Vol. 2, Issue 4, Oct. - Dec. 2011
in order to generalize it on any type of micro biological
bodies.
3. Process the most complex samples and give accurate
count.
4. Tackle any shape and size of colonies.
5. Take the shape of a product accepting different samples
and giving count of total number of colonies present,
which can be used in various biotech and other areas/
industries.
IV. Results
TEST 1 (Processed image of Non Overlapped Bacterial
Colonies)
Fig. 1: Processed image of Non Overlapped Bacterial
Colonies
Fig. 2: Total number of Overlapped Bacterial Colonies found
TEST 2(a) (Processed image of Overlapped Bacterial
Colonies)
Fig. 3: Processed image of Overlapped Bacterial Colonies
Fig. 4: Total number of Overlapped Bacterial Colonies found
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ISSN : 2230-7109 (Online) | ISSN : 2230-9543 (Print)
TEST 2 (b) (Processed image of Overlapped Bacterial
Colonies)
Fig. 10: Total number of Overlapped Bacterial Colonies found
Fig. 5: Processed image of Overlapped Bacterial Colonies
Fig. 6: Total number of Overlapped Bacterial Colonies found
TEST 2(c) (Processed image of Overlapped Bacterial
Colonies)
Fig. 7: Processed image of Overlapped Bacterial Colonies
Fig. 8: Total number of Overlapped Bacterial Colonies found
TEST 2 (d) (Processed image of Overlapped Bacterial
Colonies)
Fig. 9: Processed image of Overlapped Bacterial Colonies
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