Localized Image Segmentation and Enhancement for Meteorite Images

Yufang Bao, PhD Math and Computer Science Department

Fayetteville State University, Fayetteville, NC 28301 ybao@uncfsu.edu

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

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(a)

(b)

Figure 7. (a) The color synthesized cluster image. (b) the

gray intensity Mg image.