Engineering, 2012, 5, 57-60
doi:10.4236/eng.2012.410B015 Published Online October 2012 (http://www.SciRP.org/journal/eng)
Evaluation of Hepatic Cystic Echinococcosis’ CT image in
Xinjiang Uygur Autonomous Region based on Kolmogorov
Complexity Model*
Jingjing Zhou1, Murat Hamit1, Abdugheni Kutluk1, Chuanbo Yan1, Li Li1, Jianjun Chen1,
Yanting Hu1, Dewei Kong2, Weikang Yuan1
1College of Medical Engineeri ng Technology, Xinji ang Medical Universi ty, Urumqi, China
2Department of Radiology, The First Affiliated Hospital, Xinjiang Medical University, Urumqi, China
Received 2012
ABSTRACT
Designing and developing computer-assisted image processing techniques to help doctors improve their diagnosis has received con-
siderable interests over the past years. In this paper, we used the kolmogorov complexity model to analyze the CT images of the
health y liver and multip le dau ghter hydatid cysts. Before t he co mplexit y characteri stic cal culati ng, th e image prep rocessin g methods
had been used for image stand ard izati on . Fro m the ko lmogoro v complexit y mod el, co mplexit y char act erist ic were cal cul ated in order
to quantify the complexity, between healthy liver and multiple daughter hydatid cysts. Then we use statistical method to analyze the
complexity characteristic of those two types of images. Our preliminary results show that the complexity characteristic has statisti-
cally significant (p<0.05) to analyze these two types CT images, between the healthy liver and the multiple daughter hydatid cysts.
Furthermore, the result leads us to the conclusion that the kolmogorov complexity model could use for analyze the hydatid disease
and will also extend the analysis the other lesions of liver.
Keywords: Hepatic Cystic Echinococcosis; CT image; Kolmogorov Complexity
1. Introduction
Cystic echinococcosis (CE), also known as hydatid disease, is
an infection of larval stage animal tapeworm, Echinococcus,
has a wide geographical distribution [1]. The liver is the most
common site of the echinococcal cyst of the pastoral strains (>
65%) [2]. The greatest prevalence of cystic echinococcosis in
human and animal hosts is found in countries of the temperate
zones, including southern South America, the entire Medi-
terranean littoral, southern and central parts of the former So-
viet Union, central Asia, China, Australia, and parts of Afri-
ca[3]. In China, human CE has been recorded in 22 Provinces
and Administrative Regions, with a particularly high endemic
level over large areas of north-western provinces [4]. The Xin-
jiang Uygur Autonomous Region, multi-ethnic province in
northwestern China, is one of the most important foci of human
CE in the world [5]. The definitive diagnosis for most cases of
CE in man is by physical imaging methods, such as radiology,
ultrasonography, computed axial tomography, and magnetic
resonance imaging. The ability of CT to reveal hepatic lesions
in experiment al and clinical set tings has been well documented
and has high accuracy in the diagnosis of complications in CE
of the liver [6]. Designing and developing computer-assisted
image processing techniques to help doctors improve their di-
agnosis has received considerable inter es ts in recent years.
Medical imaging is clearly very important to extract the
maximum possible information from any image obtained. Mul-
tivesicular cysts manifest as well-defined fluid collections in a
honeycomb pattern, with multiple septa representing the walls
of the d aughter cysts. When dau ghter cysts are sep arated b y the
hydatid matrix (a materi al with mixed echogen icity), th ey show
a “wheel spoke” pattern [7]. The CT image of multiple daugh-
ter hydatid cysts showed a multiloculated appearance with the
density of fluid in the daughter cysts lower than the density of
fluid in the mother cyst. Fig. 1 shows typical a large cystic le-
sion, which included many small round shaped cystic lesions.
We propose to focuse on using Kolmogorov Complexity model
for calculate the complexity of the CT image, both the healthy
liver and the multiple daughter hydatid cysts.
The evalu ation of the complexity of an observed object is an
old but outstanding problem. The notion of Kolmogorov com-
plexity was defined independently by G. Chaitin, R. Solomono,
and A. Kolmogorov [8]. Kolmogorov proposed a complexity
measure of a specified bi nary sequence, in which the complex-
ity is given by the length of the shortest algorighm which can
produce this sequence [10]. This definition of complexity is
usually called Kolmogorov Complexity (KC). Kasper and
Schuster [11] proposed a computationally simple measure of
the algo rit hmic co mplexi t y which was anal yzed mathemat ically
by Lempel and Ziv [12]. The complexity of a finite string from
the Kaspar-Schuster’s method can be considered as a realiza-
tion of the Kolmogorov Complexity. Kolmogorov Complexity
and other complexity measures have been applied in many
fields, such as time series analysis [13], [14], handwritten cha-
racters recognition [15], image compression [9] and statistic
complexity [16]. These applications have shown that the com-
*This work was supported by the National Natural Science Founda
tion of
China, Xinjiang, 30960097 and 81160182.

J. J. ZHOU ET AL.
58
plexity measu re is a u seful tool for signal and image processing.
To author's knowledge, the present study of kolmogorov com-
plexity has not been applied in the hepatic cystic echinococco-
sis.
Based on the above observations, we proposed use the kol-
mogorov complexity model to analyze the CT image of h ealth y
liver and multiple daughter hydatid cysts (presented in Figure
1). For the purpose of complexity analysis, the original image
needs to be preprocessed. Our approach first preprocessed the
image, including color transformation, normalize the scale,
remove noise and enhance the contrast, shown in Figure 1.
Then, followed by calculate of the complexity characteristic.
From the kolmogorov complexity model, complexity characte-
ristic were calculated in order to quantify the complexity, be-
tween healthy liver and multiple daughter hydatid cysts, and the
complexity was analyzed by SPSS, the statistical analysis soft-
ware. Furthermore, we discussed the usefulness of the proposed
complexity model for analyzing a series of healthy liver and
multiple daughter hydatid cysts.
2. Methodology
2.1. Image Acquisition
Abdominal CT images, from both patients and healthy controls,
were acquired at First Affiliated Hospital, Xinjiang Medical
University of China, using a CT scanner. Digitized CT images
of the abdomen, used as input for this study, were grouped into
two classes: (a) healthy liver and (b) multiple daughter hydatid
cysts. Furthermore, a number of 200 images provided for the
experiment, 100 images of healthy liver and 100 images of
multiple daug ht e r hydat id cysts .
2.2. Image Preprocessing
A captured image contains not only the information of useful
but also some noise and the variation caused by the rotation and
translation. So, the image cannot be used directly. In order to
keep the useful information of the CT images under different
conditions, image preprocessing approaches were used to pre-
process images before the analysis algorithm was applied. For
the purpose of complexity analysis, the original image needs to
be preprocessed to color transformation, normalize the scale,
remove no ise and enhance the contrast, red uce the influence of
the factors mentioned above. The result of image preprocessing
was shown in Figure 2 and Figure 3.
Step 1
Color transformation: Convert RGB image to the grayscale
intensity image, by eliminating the hue and saturation informa-
tion while retaining the luminance. The original image is in
RGB color space. That is, the color image is formed by the
combination of three digital images of the same region shot
through red, green, and blue filters. This color space is fine for
digitization, but transformation to another color space may be
advantageous for subsequent steps in the analysis [17].
Figure 1. Description of the proposed model for complexity analysis of CT image for both healthy liver and multiple daughter hydati d cy s ts.
Figure 2. C T imag e of healthy liver. (a) A n ori g i nal image. (b) The image af ter color transformation. (c) The image a f ter normalized the scale.
(d)The image after applied me d i a n filter. (e) The image after application the CLAHE enhancing.
Figure 3. CT image of multiple daughter hyda tid cyst. (a) An original image. (b) The imag e after color transformation. (c) The imag e after
normalized the scale. (d)The image after applied me dian filter. (e) The image a ft er applic ation the CLAHE enhancing.

J. J. ZHOU ET AL.
59
Step 2
Normalizing scale: Abdominal CT image from different
people and different CT scanner may be captured in different
size. We propose to give a four-element position vector that
specifies the size and position of the crop rectangle. These im-
ages were digitized into1024
×
1024 array with spatial resolu-
tion of 256 grey levels.
Step 3
Removing noise: Median filtering is a nonlinear operation
often used in image processing and more effective than convo-
lution when the goal is to simultaneously reduce noise and pre-
serve edges. Median filtering uses a sliding window. It finds the
median value of the population of pixels inside the window,
and assigns this value to the corresponding output pixel.
Step 4
Enhancing contrast: In CT images, hydatid lesions located in
a liver are generally identified by intensity difference between
lesions and liver. The intensity of the hydatid lesions can be
lower and or higher than that of the liver. Therefore, the objec-
tive of our work is to enhance contrast of CT image containing
liver and hydatid lesions as a necessary preprocessing so that
we are able t o keep more use ful informati on in CT images. We
propose to enhance the contrast of CT image by transforming
the values using contrast-limited adaptive histogram equaliza-
tion (CLAHE) [18].
2.3. Kolmogorov Complex i ty Measu re
An image can be first converted into a one-dimensional binary
sequence by scanning it either horizontally or vertically. In our
proposal, we evaluate the complexity of each vector in the ho-
rizon tal di recti on and get th e complex valu e of each r ow vector .
Then composed the complexity of each row into a vector of
complexity and we calculate the complexity of the complex
vector as the charact er istic co mplexity of image.
For simplicity, we consider strings consisting of ‘0’ and ‘1’
only, that is, binary sequences. Assume that the complexity of a
finite string of length N is de noted by c(N).
Theoretical study has shown that [11],
)(
log
)(
2
Nb
N
N
Nc ≡≤
(1)
( )( )
cN bN=
only when N is sufficient large. The KC
measure is defined as
( )( )( )
2
1logKCcN bNcNN
N
= =
(2)
Obviously, we have
01KC≤≤
.
Kolmogorov only gave a general definition of the Kolmogo-
rov Complexity. To compute the KC measure, Kaspar and
Schuster proposed an explicit algorithm [13]. In their approach,
two operations, copying and inserting: were used for computing
( )
cN
. Let
( )
1
N
i
si
=


denote a given string of length N. The
algorithm is given below:
1. Set c = 1 (the compl exi ty
( )
cN
) and
1j=
.
2. Set
0i=
, 1
k
=, and
max
1k=
.
3. If
() ()
siks jk+= +
(a copy operation only),
( )
1kk+→
, go to 4; otherwise (an inserting operation
required) go to 5.
4. If
( )
jk N+>
,
( )
1cc+→
(the last copy step is
counted), stop; otherwise go to 3.
5. If
max
kk>
,
max
kk=
.
6.
( )
1ii+→
.
7. If
ij≠
, 1k=, go to 3.
8.
( )
1cc+→
(counts the number of inserting opera-
tion) and
max
jk j+→
.
9. If
( )
1
jN
+>, stop; otherwise go to 2.
where c is used to count the number of inserting operations, that
is, the complexity of the sequence
( )()
1
,
N
i
si cN
=


. Note that
the last copy step is also counted if measure is then calculated
using Equation 2.
3. Experimental Result
We present the preliminary results of our studies on the useful-
ness of the proposed complexity model for analyzing a series of
healthy liver as shown in Figure 2 and multiple daughter hyda-
tid cysts as shown in Figure 3.
3.1. Descriptive Statistical Analysis of Kolmogorov
Complexity Characteristic
In Kolmogorov complexit y chara cteristic, ther e are 200 images
available, 100 healthy liver images and 100 multiple daughter
hydatid cyst images. According to the calculated complexity,
we use the statistical analysis software: SP SS, descri ptive anal-
ysis the complexity dat a. The sample size, mean value, standard
deviation and standard error of mean for both healthy liver and
multiple daughter hydatid cysts are given in Table 1. The box
plots (shown in Figure 4) depicted th e minimum, first quartile,
median, third quartile and maximum of healthy liver and mul-
tiple daughter hydatid cysts.
Table 1. Descriptive statistical analysis of complexity data for
healthy liver and multiple daughter hydatid cysts.
group N Mean Std. Deviation Std. Error Mea n
Healthy liver 100 0.31063 0.05631 0.00563
Multiple daughter
hydatid cysts 100 0.31334 0.07649 0.00765
Figure 4. Box plots of healthy liver and multiple daughter hydatid
cysts.

J. J. ZHOU ET AL.
60
3.2. Independent-Samples T Test f or Complexity
Characteristic
To evaluate t he effecti venes s o f the co mplexit y data, we use th e
statistical analysis method of Independent-Samples T Test to
analyze the complexity data. In the result, the significance
(p=0.002) is less than 0.05 indicating that the complexity cha-
racteristic has differences between healthy liver and multiple
daughter hydatid cysts.
4. Conclusion
In this paper, we propose an image analysis technique based on
Kolmogorov Complexity model for characterizing healthy liver
and multiple daughter hydatid cysts. A statistical description
and analysis of complexity characteristic for two types of CT
images has been presented. Preliminary experimental study
demonstrated the feasibility of the proposed technique in analy-
sis the healthy liver and multiple daughter hydatid cysts.
5. Acknowledgements
The authors would like to acknowledge the suppoting of the
First Affiliated Hospital of Xinjinag Medical University, PR
China.
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