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Localized Image Segmentation and Enhancement for Meteorite Images
Yufang Bao, PhD Math and Computer Science Department
Fayetteville State University, Fayetteville, NC 28301 [email protected]
ABSTRACT
This paper proposed an image enhancement and
segmentation algorithm to localize the segment of interest in a
series of images obtained from the Electron Microprobe. There
are totally four different images that are corresponding to the
spatial density distributions of four major chemical elements
inside a meteorite rock surface. The density distribution of
each chemical element is shown in a gray valued image with a
resolution in the micrometer range. Our algorithm applied a
statistical image enhancement technique to improve the
visualization of features about the same segment of interest in
three images using one image as a reference. The three
chemical elements distributions of a localized cluster were
enhanced, and eventually integrated together into a synthetic
color image to reflect various chemical compound distributions
insider this cluster.
Keywords
Image enhancement, segmentation, meteorite images.
1. INTRODUCTION The methods for analyzing images often
varied according to the different features in images
acquired from various imaging instruments. In the
case of studying the chemical compound in the
ureilites, the typical small size (about 1x1 in2) of
ureilites used for analysis has made measurement
extremely difficult for scientists. The measurement is
powered by using a new generation imaging system
called Electron Microprobe. The Electron
Microprobe allows scientists to acquire a set of high
spatial resolution gray images. Each shows the
concentration of one chemical element. In this paper,
four images of chemical distributions of a ureilite
rock are acquired from the Electron Microprobe. The
images represent the spatial density distributions of
four chemical elements, Magnesium (Mg), Iron (Fe),
Aluminum (Al), and Calcium (Ca). The Field of
view (FOV) of each image covers the polished
surface of ureilite with a resolution in a micrometer
(mm) range [1]. The high intensity pixel value in an
image shows high chemical concentration, while a
low intensity value shows a low chemical
concentration in a pixel. The four chemical element
images of the ureilite have the following features.
The intensity values of the Mg image are grouped by
clusters; hence, the overall image is of bright and
high contrast as shown (Fig. 1). This is consistent
with the existing knowledge of the rich Mg
concentrations inside a ureilite in general. The Iron
(Fe), Aluminum (Al), and Calcium (Ca) images
usually were very dark inside each cluster due to
their lower concentrations in general, and the low
intensity values shadowed the boundaries between
adjacent clusters as shown in Fig. 2.
It is not practical to study directly the whole
spatial content of the images while, often, only a
small cluster was of interest. The images acquired
from an Electron Microprobe usually are of large
size with around 1,000 x 1,500 pixels in total for a
ureilite roack of size 1x1 in2. Not only is the
computational burden a concern, but also the rich
intensity scales inside a large size image will degrade
the view of the cluster of interest if the whole image
is directly studied. Due to its simultaneous
acquisition feature, a pixel in the same location in
each image usually accounts for the same position in
the rock surface. It is useful to use one chemical
element image, such as the Mg image, as a reference,
to identify the segments of interest in the rest images
to reveal the material composition in the rock
surface. This is useful for analyzing all chemical
element distribution images simultaneously in order
to study the highly correlated features inside the
images. Another reason for narrowing into a small
segment of the images for study is that the rich
intensities coexisted in the image makes it difficult to
enhance the features using the existing algorithms. It
is rational to search for a small cluster of interest for
enhancement to avoid the complexity with
enhancement of the entire dark image (see fig. 2)
In this paper, we first described an
interactive method to select a cluster of interest in the
rock surface by using the bright and high contrast
Mg image as a reference image. The location of the
selected cluster was then mapped into the distribution
images of the other chemical elements. The low
intensity contrast inside the cluster will be selected
for further processing. Secondly, we describe a
method to correct the gray values of the low intensity
images by constraining to this small cluster. The
interior pixels inside this cluster only count for a
fraction of the original image size. An improved
contrast within this cluster will be locally maximized.
Finally, we proposed to synthesize all the enhanced
segments of three major chemical elements into a
color image to characterize the chemical compounds
to show the microscopic view of a cluster inside the
rock surface.
2. CLUSTER SEGMENTATION
Image segmentation is often associated with
edge detection. Often, a closed edge formed with
connected curves is of interest, which typically
defines the boundary of a segmented object.
However, most of edge detection methods are simply
based on either the gradient or the zero crossing
information and therefore are not geometrically
oriented. The gradient information is used for edge
detection by defining edges as the local directional
maxima of the absolute gradient magnitude
computed. and is represented by the Sobel edge
detector [2] and Canny Edge Detector [3]. The zero-
crossing information is used for edge detection by
computing a second-order derivative of the image
values [9].
The tradition edge detection methods are
capable of detecting the potential edges faithfully
showing all the high constrast locations in the image
[2-3, 7]; however, it is not suitable when our goal is
to identify a cluster in the meteorite image. Our
interest is to label only the cluster of image for
analyzing the chemical compounds inside the cluster,
while neglecting some small segments inside this
cluster.
Meteorite images are featured by their
massive size (Fig. 1) with different clusters
integrated together. Each cluster was grouped by
pixels with similar intensity values while the
boundaries are marked with relatively different
intensity values. As we mentioned earlier, the Mg
image is the best candidate for edge detection. This
image is used to identify a cluster of interest to allow
enhancement [4] to be applied efficiently to this
cluster in the other images.
Our proposed edge detection technique is
based on the Sobel detector and modified
morphology methods [8]. To begin, we select a
rectangle segment on the reference, Mg, image.
Inside the rectangle region, the histogram of the
image is generated. The mean of the histogram is
used as an automatic threshold level for generating a
binary image where the pixels of the potential
boundaries of a cluster object are marked as 1, and
others are marked as 0. The initial operation of this
thresholding using the Sobel detector finds all the
potential boundaries blindly, which includes
boundaries of very small segments. Based on this
potential boundary image, B, the shortest Euclidean
distance of each pixel to the potential boundary in
Figure 1: The Mg image to be used as a reference.
Figure 2: The Fe image to be enhanced.
image B is calculated, i.e. for each pixel represented
as (i, j), d(i, j) is defined as
)),(),((inf),(2),(
lkjijidBlk
−=∈
The boundary image, B, is updated to unify
the multiple broken boundaries that belong to the
same edge:
( )
( )
≥
<=
thresholdlkd
thresholdlkdlkB
, if ,0
, if ,1),(
where the threshold is set for number 3. Note this
threshold is different from the threshold used for
edge detection. The binary value of 1 in the image, B,
indicates the improved boundaries. This updated
boundary allows multiple boundaries for the same
segment to be unified together to show the
geometrical features.
Finally, the largest connected component is
identified; To do so, we identified all the connected
components in the edge images, and then defined the
size of each connected component, kO , as
∑∈
=
kOyx
kyxIoM
),(
),()(
The largest component to be preserved is defined as
)(maxarg kO
oMOk
=
This method discarded all the isolated small
segments initially being treated as boundaries. In
addition, the morphologically operations, namely, a
dilation followed by an erosion, are performed to
close the potential gaps inside the boundaries, while
to ensure the boundaries for a cluster are properly
connected. In so doing, the major boundaries are
identified. It separates the Mg image into several
clusters that we are interested in. Only the inside of
the cluster of interest was selected and preserved.
The major steps for finding a cluster of interest for
the Mg image is shown in Fig. 3.
3. ENHANCEMENT Enhancement of a low contrast chemical
element image allows us to visualize the spatial
density distribution of the compounds made from this
element, and provides a base for counting the
concentrations. When an image is in low contrast, the
image enhancement sometimes involves deblurring
and noise removal procedures [5]. The histogram of
an image statistically demonstrates the the image
intensity distribution and is typically the base for
contrast correction. Direct contrast adjustment
methods [2] and histogram equalization methods [6-
7] are two methods generally used for gray image
enhancement. To better preserve the chemical
element concentrations, histogram equalization
methods is used in our study. The histogram
equalization methods map the input gray levels to the
output gray levels which are evenly dispersed based
on statistical knowledge. The existing image
enhancement algorithm is improved to enhance the
distribution of each chemical element image.
Figure 3. Our proposed cluster segmentation
The traditional histogram equalization
methods adjust the image intensity globally. It treats
the entire image equivalent and usually yields poor
local performance; therefore, in a localized region,
the intensity may not be appropriately separated even
after enhancement. Several improvement algorithms
to enhance the intensity in a localized region have
since proposed [6, 8].
An image enhancement algorithm is ideal
only when it matches the unique image features;
hence, when referring to a chemical element
distribution image of the ureilite acquired from the
Microprobe, the special features of these images
needed to be considered. After the cluster of
interested was segmented, image content inside the
cluster was kept. Inside this cluster, Our
a) Select a rectangle
area from the original
Image.
b) A binary image was
generated by applying Sobel
detection with an automatic
threshold.
c) The largest connected
component with new
boundaries defined and
morphological operation.
d) An interactive
selection of the cluster
with closed contour.
enhancement technique combines the Gaussian
smooth filter together with a modified histogram
equalization method to map the majority of the image
intensities into the maximum dynamic display range
available.
We proposed to partially equalize the gray
values Histogram equalization (HE) method. HE
method is a statistical analysis of image intensities.
The histogram of a digital image counts the number
of pixels whose values are inside a set of ranges
called bins. If we use ( )yxI , to represent an image,
a histogram is defined as a discrete function
( ) n,=highyxIlowpixelsiHii
,1i,),(#)( L≤<=
The (i
low , i
high ) is the range of the i-th bin, and n
is the total number of bins used. The relative
frequency histogram approximately defines the
probability of occurrence at the i bin, as
n,=iPN
iH,1i,)(
)(L=
where N is the total number of pixels in an image
taking into considerations.
A good enhancement result will have the histogram
stretched to the highest dynamic display range. The
histogram of pixels inside the selected cluster tends
to gravitate to the lower intensity within a narrow
range with outliers in both the high and low end in
the low contrast chemical element images that we
aim to enhance. The outliers are classified
dynamically as
( ) ( ) } )(ifor ,)( if , ,{
Outliers
nlowest
1
εε <<∈
=
∑∑== highestii
iPiPiBinyxI
with the lowest and highest represent the landmark
values used to define the outliers, which can be set to
about three times the inter quartile range (3IQR)
away from the median. In our case, we use the 0.1%
data as outliers. After the outliers are classified, we
set the
( ) ( )
( ) ( )iBinyxIiPhighestyxI
iBinyxIiPlowestyxI
highesti
i
∈<+=
∈<−=
∑
∑
=
=
, and , )(if ,1),(
, and ,)( if ,1),(
n
lowest
1
ε
ε
In addition a Gaussian smooth filter is applied for
preprocessing to make sure the outliers are properly
classified and a histogram of best fit can be resulted.
Our new histogram image enhancement steps are:
1. Determine the lowest 0.1% outliers in the
histogram.
2. Determine the highest 0.1% outlier in the
histogram.
3. Generate new histogram using the intensity
constrained between the lowest and highest
outliers defined.
4. Apply histogram equalization.
(a)
(b)
(c)
Figure 4: The Fe image segment (a) and the
cluster identified (b), followed by the enhanced
clusters (c) .
In Fig. 4(a) is shown the cluster of Fe image.
In Fig. 4(b) is shown the cluster of Fe image after the
HE is directly applied; the cluster is marked in green
color. In Fig. 4(c), our partial HE enhancement
technique is applied; the cluster is marked red color.
The resulting image in Fig. 4(c) has higher contrast
than the image in Fig. 4(b). This is further shown in
the histogram in Fig. 5, where the histogram of the
Fig. 4(b) is shown in the Fig. 5 (b). The histogram of
Fig. 4(c) is shown in Fig. 5 (c) with Gaussian filter
applied. Fig. 5 (c) that is corresponded to the partial
HE method has shown improved histogram with our
partial HE method.
Figure 6. The diagram for generating the color synthesized
cluster image.
5. SYNTHESIZED COLOR IMAGE
Human eyes perceive color from three types
of cones in their retina-Red ( R), Green (G) and Blue
(B), corresponding to the three basic colors RGB. A
color image is represented in (R, G, B) vectors. We
can differentiate just noticeable difference in terms of
little changes in color distance, which makes the
color image rich to interpret.
Here, we have composed a color image using
the three chemical element image clusters. The color
images are generated after the segmentation and
enhancement are obtained. A color image is the
composed of vector (R, G, B) for each image pixel.
Here we designed the color vector (R, G, B)=(Fe,
(a)
(b)
(c)
Figure 5: The histogram of the original Fe
image cluster (a); the histogram of the cluster
that histeq was directly applied (b); the
histogram of our enhanced cluster (c).
Mg Image
cluster
identified
Fe Image Al Image Cluster
segmentation
algorithm
Identify the same cluster in
the Fe and Al images
Localized enhancement
applied to both clusters
Synthesized Color image output
Enhanced Fe
cluster image
Enhanced Al
cluster image
Mg, Al). The whole processing is shown is the
diagram in Fig. 6.
The synthesized color image is shown in Fig.
7 (a), in comparison to the Mg gray intensity image.
The yellow color shown in the image likely indicated
rich iron content region. This distribution color
allows us to further study the compound distribution
and display the distribution of different chemical
compounds simultaneously in a color image.
6. CONCLUSIONS In this paper, we have proposed to localize
the cluster of interest in the meteorite images using
the Mg image as the reference. We further applied an
image enhancement to improve the visualization of
the low contrast images, and eventually synthesized
the three images of higher contrast into a color image
after the segmentation and enhancement.
7. ACKNOWLEDGMENTS Our thanks to Dr. Steven Singletary for his
help with the images provided. We also thank Miss
Cassandra Hall and Miss Siera Gonzales. Both are
students participated in the CPSER program in
Fayetteville State University, and have contributed to
this research when they are participated in the
mentored summer research project sponsored by the
Center for Promoting STEM Education and
Research.
8. REFERENCE
[1] S. J. Singletary and T. L. Grove, "Early
petrologic processes on the ureilite parent body,"
Meteoritics & Planetary Science, vol. 38, pp.
95-108, Jan 2003.
[2] R. C. Gonzalez and R. E. Woods, Digital image
processing, 2nd ed. Upper Saddle River, N.J.:
Prentice Hall, 2002.
[3] J. Canny, "A Computational Approach to Edge-
Detection," Ieee Transactions on Pattern
Analysis and Machine Intelligence, vol. 8, pp.
679-698, Nov 1986.
[4] Y. F. Bao and H. Krim, "Smart nonlinear
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Transactions on Pattern Analysis and Machine
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[5] J. L. Lehr and P. Capek, "Histogram
equalization of CT images," Radiology, vol.
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[6] J. S. Tang, et al., "Image enhancement using a
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[7] J. A. Jiang, C. L Chuang, Y.L. Lu and C.S.
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contrast regions", IET Image Processing, Vol 1
(3) pp. 269-277, 2007
[8] C. Leung, K. Chan, H. Chan, W. Tsui, "A new
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1986.
(a)
(b)
Figure 7. (a) The color synthesized cluster image. (b) the
gray intensity Mg image.