A New Approach for Biomedical Image Segmentation:
Combined Complex-Valued Artificial Neural Network
Case Study: Lung Segmentation on Chest CT Images
Murat Ceylan, Yüksel Özbay, Erkan Yıldırım

Abstract— The principal goal of the segmentation process is to
partition an image into classes or subsets that are homogeneous
with respect to one or more characteristics or features. In
medical imaging, segmentation is important for feature
extraction, image measurements, and image display. This study
presents a new version of complex-valued artificial neural
networks (CVANN) for the biomedical image segmentation.
Proposed new method is called as combined complex-valued
artificial neural network (CCVANN) which is a combination of
two complex-valued artificial neural networks. To check the
validation of proposed method, lung segmentation is realized.
For this purpose, we used 32 chest CT images of 6 female and
26 male patients. These images were recorded from Baskent
University Radiology Department in Turkey. The accuracy of
the CCVANN model is more satisfactory as compared to the
single CVANN model.
I. INTRODUCTION
S
egmentation that is one of the most difficult tasks in the
image processing subdivides an image into its constituent
characteristics or features. Accuracy of segmentation
determines the success of image analysis process. In other
words, error in segmentation level of complete computerized
analysis procedures will impact all higher level activities. In
many medical image processing applications, segmentation
may be useful to detection of organs such as brain, heart,
lungs or liver in computerized tomography (CT) or magnetic
resonance (MR) images, while in others into distinguish
pathological regions, such as tumor, normal tissue, tissue
deformities. A wide variety of segmentation techniques has
been proposed in the literature [1-2]. However, there is no
one standard segmentation technique that can produce
satisfactory results for all imaging applications. Compared
with the classical methods in the literature, the ANN
approach has the advantage of paralel processing (with
appropriate hardware), robustness, noise tolerance, and
adaptability. Neural networks provide pixel classification
paradigm that can be used for image segmentation [3-4].
Manuscript received July 25, 2010. This work was supported by the
Coordinatorship Selcuk University’s Scientific Research Projects.
Murat Ceylan is with the Electrical and Electronics Engineering
Department, Selcuk University, Konya, 42075 TURKEY (phone: +90-3322232042; fax: +90-332-2410635; e-mail: mceylan@selcuk.edu.tr).
Yüksel Özbay is with Electrical and Electronics Engineering Department,
Selcuk
University,
Konya,
42075
TURKEY
(e-mail:
yozbay@selcuk.edu.tr).
.Erkan Yıldırım is with Radiology Department, Baskent University,
Ankara, TURKEY, (e-mail: drerkany@yahoo.com).
Neural network based segmentation approaches may provide
good results for medical images with considerable variance
in structures of interest [3]. Sha and Sutton [5] proposed a
neural network system for segmentation and classificiation of
digital brain images. The other application of neural network
for automatic segmentation was done by Nattkemper et al.
[6]. Papadopoulos et al. [7], was used a hybrid neural
network, which consist of two components: a rule-based and
a neural network. In [8], ultrasound images were segmented
using hybrid neural network. Vilarino et al. [9], were applied
cellular neural network to image segmentation based on
active contour techniques. Middleton et al. [10], used
combination of neural networks and active contour models
for segmentation of MR images. In [11], region-based
segmentation and neural network edge detection were used.
For MR, CT and ultrasound images segmentation, a new
method which called as incremental neural network was
developed by Dokur [12] and Kurnaz et al. [13 ]. Wismuller
et al. [14] and Ong et al. [15] were used self-organized
model for the segmentation.
A complex-valued artificial neural network (CVANN) is
neural network which consist of complex-valued inputs,
weights, thresholds, activation functions and outputs.
CVANN have been widening the scope of applications not
only in signal processing [16] but also in image processing
[17]. For complex signal and image processing problems,
many existing neural networks cannot directly be applied.
Although for certain applications it is possible to reformulate
a complex signal processing problem so that a real-valued
network and learning algorithm can be used to solve the
problem, it is not always feasible to do so.
In this paper, a novel cascade structure is proposed, called as
combined complex-valued artificial neural networks
(CCVANN). General scheme of CCVANN is implemented
in two levels. In the first level, learning of CVANN is
realized using the original data set. After that, prediction of
the first level and target of the original data are presented to
the second level CVANN as inputs. In this study,
segmentation of lung images is realized using CCVANN.
II. MATERIAL AND METHODS
A. Image Data
For the development and evaluation of the proposed
system we used our image collection (32 chest CT images of
6 female and 26 male patients). These images were recorded
from Baskent University Radiology Department in Turkey
[17]. This collection includes 10 images with benign nodules
and 22 images with malign nodules. Averaged age of
patients is 64. Each CT slice used in this study has
dimensions of 752 x 752 pixels with grey level.
B. Complex Wavelet Transform (CWT)
Wavelet techniques are successfully applied to various
problems in signal and image processing [18-19]. It is
perceived that the wavelet transform is an important tool for
analysis and processing of signals and images. In spite of its
efficient computational algorithm, the wavelet transform
suffers from three main disadvantages. These disadvantages
are shift sensitivity, poor directionality and absence of phase
information. CWT overcomes these disadvantages [20].
Recent research in the development of CWTs can be
broadly classified in two groups; RCWT (Redundant CWTs)
and NRCWT (Non-redundant CWTs). The RCWT include
two almost similar CWT. They are denoted as DT-DWT
(Dual-Tree DWT based CWT, with two almost similar
versions namely Kingsbury’s and Selesnick’s [20]. In this
paper, we used Kingsbury’s CWT [20] for feature extraction
of image to be segmented.
C. Combined Complex-Valued Artificial Neural Network
(CCVANN)
In this study, a complex back-propagation (CBP) algorithm
has been used for pattern recognition. We will first give the
theory of the CBP algorithm as applied to a multi layer
CVANN. Figure 1 shows a CVANN model. The input
signals, weights, thresholds, and output signals are all
complex numbers. The activity Yn of neuron n is defined as:
(1)
Yn   Wnm X m  Vn
Use the formula in (3) to calculate output signals.
4. Adaptation of weights and thresholds
If this condition is satisfied, algorithm is stopped and weights
and biases are frozen.
N
  Tn
p
n 1
( p)
 On( p )
2
 10 1
(4)
where Tn(p) and On(p) are complex numbers and denote the
desired and output value, respectively. The actual output
value of the neuron n for the pattern p, i.e the left side of (4)
denotes the error between the desired output pattern and the
actual output pattern. N denotes the number of neurons in the
output layer [21].
Fig. 1. CVANN model
The general CCVANN model which is a combination of two
CVANN used in this study is illustrated in Fig. 2. The
CVANNs were used at the first and second levels for the
complex-valued pattern recognition.
m
where Wnm is the complex-valued (CV) weight connecting
neuron n and m, Xm is the CV input signal from neuron m,
and Vn is the CV threshold value of neuron n. To obtain the
CV output signal, the activity value Yn is converted into its
real and imaginary parts as follows:
(2)
Yn  x  iy  z
where i denotes  1 . Although various output functions of
each neuron can be considered, the output function used in
this study is defined by the following equation:
(3)
fC ( z )  f R ( x)  i. f R ( y)
where fR(u)=1/(1+exp(-u)) and is called the sigmoid
function.
Summary of CBP algorithm:
1. Initialization
Set all the weights and thresholds to small complex random
values.
2. Presentation of input and desired (target) outputs
Present the input vector X(1), X(2),….,X(N) and
corresponding desired (target) response T(1), T(2),….T(N),
one pair at a time, where N is the total number of training
patterns.
3. Calculation of actual outputs
Fig. 2. CCVANN model
III. MEASUREMENT FOR PERFORMANCE EVALUATION
In this paper, we used an algorithm to evaluate the
classification results of CVANN’s outputs for training and
test images that include lung region pixels and surrounding
pixels of lung-region [17]. Number of correct classified
pixels in segmented image (752x752) was calculated
according to the following algorithm:
IF
I T - IO = 0
THEN
Number of correct classified pixel
(CCP) increase by 1
ELSE
Number of incorrect classified pixel
(iCCP) increase by 1
where, IT and IO are target of network and actual output of
network, respectively. Finally, we measured the accuracy of
proposed methods using following equation:
Accuracy (%) = (CCP / (CCP + iCCP)) x 100
(5)
IV. RESULTS AND DISCUSSION
In this paper, segmentation of lung region was realized using
CWT based CCVANN. Complex wavelet transform was
used to reduce the size of input matrix of training and test
images. In proposed cascade structure, feature vector of
original CT images (size of 752x752) was extracted using
CWT with 2nd, 3rd and 4th level. Size of feature vectors was
obtained as 188x188, 94x94 and 47x47 for 2 nd, 3rd and 4th
level, respectively. Obtained complex-valued feature vectors
were presented as inputs to CCVANN. Figure 3 shows block
representation of proposed method. The complex-valued
backpropagation algorithm was used for training of the
proposed networks.
Fig. 4. Lung images with benign nodules (a) (image no: B10)
and malign nodules (b) (image no: M16) and their results, c
and d, respectively.
To compare with lung segmentation using “single” CVANN,
learning rate, number of hidden nodes and maximum
iteration number of first CVANN was chosen as 0.1, 10 and
10, respectively, similarly [17]. These parameters for second
CVANN were determined via experimentation. Network
structures are given in Table 1.
TABLE I
NETWORK STRUCTURES
Network
Learning
Rate
1th CVANN [17]
2nd CVANN
0.1
1e-6
No. of
Hidden
Nodes
10
40
No. of
Iteration
10
10
Results of CCVANN and CVANN [17] with three levels
CWT were presented in Table 2 and Table 3. These results
are given for best and worst accuracy rates of CVANN. In
Table 2, B signify an image with benign nodule and M
signify an image with malign nodule. According to Table 2
and Table 3, CCVANN can segment lung region better than
CVANN for all of the CWT’s level.
Fig 3. Block representation of proposed method for lung
segmentation.
The training of CWT-CVANN was stopped when the error
goal (4) was achieved. In training phase, half of total 32
images were used. After that, the performance of this
network was tested by presenting other 16 test images.
Averaged accuracy rate of test is calculated as 99.80 %.
Fig. 4 shows the segmented images using 4th level CWT and
CCVANN with best accuracy rate.
V. CONCLUSIONS
In this paper, a combined complex-valued artificial neural
network model is proposed for biomedical image
segmentation. Following conclusions may be drawn based on
the results presented;
1. The results of the CCVANN compared to experimental
results are found to be more satisfactory. The proposed
CCVANN methods’ results have a lower number of incorrect
classification pixels than using a single CVANN model.
2. Although the performance of the developed CCVANN
model is limited to the range of input data used in the
training and testing process, the method can easily be applied
with additional new set of data.
ACKNOWLEDGMENT
TABLE II
COMPARISON OF CVANN AND CCVANN
USING NUMBER OF CCP AND iCCP
Number of CCP
CWT
This work is supported by the Coordinatorship of Selcuk
University's Scientific Research Projects.
REFERENCES
Number of iCCP
[1]
Image No.
Level
CVANN
CCVANN CVANN CCVANN
B10 (best)
564279
564477
1225
1027
B1 (worst)
563483
563609
2021
1895
M16 (best)
564150
564255
1354
1249
M19 (worst) 563483
563732
2021
1772
[2]
[3]
[4]
2
[5]
B10 (best)
564322
564380
1182
1124
B1 (worst)
562808
562942
2696
2562
M6 (best)
564226
564460
1278
1044
M10 (worst) 563548
563907
1956
1597
[6]
[7]
3
B10 (best)
564169
564559
1335
945
B1 (worst)
561201
561480
4303
4024
M16 (best)
564079
564463
1425
1041
M4 (worst)
563349
563872
2155
1632
4
TABLE III
COMPARISON OF CVANN AND CCVANN
USING ACCURACY RATE
[8]
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Accuracy (%)
CWT
Image No.
Level
[14]
CVANN
CCVANN
B10 (best)
99.7834
99.8184
B1 (worst)
99.6426
99.6649
M16 (best)
99.7606
99.7791
M19 (worst)
99.6426
99.6867
B10 (best)
99.7910
99.8012
B1 (worst)
99.5233
99.5470
M6 (best)
99.7740
99.8154
M10 (worst)
99.6541
99.7176
[19]
B10 (best)
99.7639
99.8329
[20]
B1 (worst)
99.2391
99.2884
M16 (best)
99.7480
99.8159
M4 (worst)
99.6189
99.7114
2
[15]
[16]
[17]
3
[18]
[21]
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