Automatic Construction of Image Transformation Algorithms
Using Feature Based Genetic Image Network
Yuta Nakano, Shinichi Shirakawa, Member, IEEE, Noriko Yata, and Tomoharu Nagao, Member, IEEE

Abstract— Image processing and recognition technologies
are becoming increasingly important. Automatic construction
methods for image transformation algorithms proposed todate approximate adequate image transformation from original
images to their target images using a combination of several
known image processing filters by evolutionary computation
techniques. In this paper, we introduce the adaptive image processing filters that process according to the features of an input
image. The processing of the adaptive filters is decided basedon the local features of an input image. We implement themto feed-forward genetic image network (FFGIN) that is one of
the automatic construction methods for image transformations.
Then we apply our method to the problems of segmentationof organs and tissues in medical images. Experimental results
show that our method constructs the effective segmentation
algorithms that extract multiple regions respectively.

I. I NTRODUCTION
Image processing and recognition technologies are becoming increasingly important, e.g., medical, industrial, andsecurity images. Numerous image processing algorithms
have been proposed and shown their effectiveness in various
fields. It is, however, difficult to design effective image
processing algorithms for given target problems because each
image processing algorithm considerably depends on thetarget problems. Therefore, automatic construction methods
for image processing algorithms are effective to given target
problems are required.
Evolutionary computation (EC) techniques have been applied to automatic construction of image processing algorithms. Genetic programming (GP) [1], [2] is often applied
to image classification tasks [3], [4], [5]. GP is also used
to develop useful texture-feature extraction algorithms [6],[7]. In these studies, the evolved features are either atpar or outperform human-designed features. Naturally, EC
techniques are used for medical image processing fields [8],[9]. The Automatic Construction of Tree structural Image
Transformation (ACTIT) system [10], [11], an automaticconstruction method for image transformation, has been
proposed previously. ACTIT approximates adequate imagetransformation from original images to their target imagesby a combination of several known image processing filters.
ACTIT constructs tree structured image processing filters
using GP. It has constructed various types of image transformation algorithms automatically. The genetic image network
Yuta Nakano, Shinichi Shirakawa, Noriko Yata, and Tomoharu
Nagao are with the Graduate School of Environment and Information Sciences, Yokohama National University, 79-7, Tokiwadai,
Hodogaya-ku, Yokohama, Kanagawa 240-8501, Japan (phone: +81 45
3394136; email: nakanock@gmail.com, shinichi.shirakawa@gmail.com,
yata@nlab.sogo1.ynu.ac.jp, nagao@ynu.ac.jp).

(GIN) is a recent automatic construction method for imagetransformation [12], [13]. The representation of GIN is a
network structure.
In this paper, we introduce the adaptive image processing
filters that match for automatic construction method. The
processing of the adaptive filters is decided based on thelocal features of an input image. We implement them to feedforward GIN (FFGIN) [13] and modify it to be parametertunable. Then we apply it to the problems of segmentationof organs and tissues in medical images. It is not possible tosegment target regions only by the intensity of the pixels inthese problems. We solve these problems using the parametertunable FFGIN.
The next section of this paper is an overview of several related works. In section III, we describe our proposed method.
Next, in section IV, we apply the proposed method to the
problems of automatic construction of image transformation
for medical images and show several experimental results.
Finally, in section V, we describe conclusions and futurework.
II. R ELATED W ORKS
A. Automatic Construction of Image Transformation
In image processing, it is difficult to select filters satisfying the transformation from original images to their target
images. The ACTIT system [10], [11] has been proposed previously. ACTIT approximates adequate image transformationfrom original images to their target images by a combinationof several known image processing filters. ACTIT constructs
tree-structured image processing filters using GP. The individual in ACTIT is a tree-structured image transformation.
The terminal nodes of a tree are the original images, whereas
the non-terminal nodes are several kinds of image processing
filters. A root node represents an output image. Users provide
training images, and the ACTIT system automatically constructs appropriate image processing procedures. 3D-ACTIT[11] is an extended method that automatically constructs
various 3D image processing procedures, and is applied to
medical image processing and video image processing [14],[15].
Recently, two other extensions of ACTIT, genetic image
network (GIN) [12] and feed-forward genetic image network(FFGIN) [13], have been proposed. Instead of a tree representation, they are represented by a network structure. Theirbiggest difference from ACTIT is the structure of connections
between image processing filters. In general, a network structure theoretically includes a tree structure (i.e., a network
structure also represents a tree structure). Therefore, thedescriptive ability of a network representation is higher thanthat of a tree structure. In GIN and FFGIN, a genotype is astring of integers that indicate image processing filter types
and connections. Other studies show that GIN and FFGIN
automatically construct a simple structure for complex imagetransformation using their network representation [12], [13].
The GIN for Image Classification (GIN-IC) [16] is the extended method of GIN, which constructs image classification
algorithms containing image transformation (preprocessing)parts.
Other approaches using linear genetic programming (LGP)
succeed in automatic construction of image transformations
and recognition [17], [18], [19]. Bhanu and Lin evolve
composite operators for object detection from combinationsof primitive image processing operators [20]. The image
segmentation programs for a biomedical image are also
evolved by combining ordinary image processing operators in[21]. Colton and Torres evolve an image filter to approximate
an artistic image filter such as a filter in Photoshop using
tree-based GP [22].

Pixel of interest

Window
Input image

Calcurate features
Feature images
Mean value

ModeStandard deviation

͐Fig. 1.

Calculation of local features using sliding window.
Feature ﬁlter

Input image

B. Prospects of Previous Works
The automatic construction methods for image transformation such as ACTIT, GIN, and other techniques show
the effectiveness in various tasks. The simple well known
image processing filters are prepared in these techniques,e.g., mean filter, maximum filter, minimum filter, Sobel filter,
Laplacian filter, gamma correction filter, binarization, linear
transformation, difference, logical sum, logical prod, and soon. We, however, consider that they have following problems:
It is necessary to prepare the image processing filters
beforehand (The processing of prepared filters is fixed).
• It tends to construct image transformations that overfit
to the training images.
•

These problems are often observed when applying them tothe medical image processing in which texture pattern isimportant. The first problem means that we have to prepare
sufficient filters to solve the given target problems. However,
it is difficult to know the effective image processing filtersfor given target problems beforehand. The second problem
suggests that the evolutionary optimization only performs
to approximate to the target images without considering
the features of the images. To overcome these problems,
we believe that the adaptive image processing filters thatconsider the features of the input images are effective. It
is possible to construct the image transformation proceduresthat consider the features of the original images and thetransformed image.
III. M ETHODOLOGY
A. Adaptive Image Processing Filters Based on Local Features of Image
Here, we describe our proposed adaptive image processing
filters based on local features of image.

Output image
CalcuratefeatureImagetransform

Original image

Fig. 2.

Filtering using calculated local features.

1) Calculation of Local Features: We do not calculate one
feature from the entire image but calculate the local featuresof each pixel to grasp the feature of each tissue. We prepare
the sliding window considering the region of a pixel andaround it. The sliding window is scanned in the image, andfeatures in the window (local features) are calculated. In our
method, we prepare the local texture features to grasp thefeatures of each organ and tissue in medical images. These
local features are useful to the segmentation of each organ
and tissue that is difficult to extract only by pixel intensities.
Fig. 1 shows the example of the images calculated local
features in PET (positron emission tomography) images.
The red color indicates high feature values and the blue
color indicates low feature values in feature images. We canobserve the different feature between the feature images andthe original image.
2) Filtering Using Calculated Local Features: Imagefiltering is composed by the local features using sliding
window described in III-A.1. Image transformation method isdecided based on the values of the calculated local features.
Concretely, the calculated values of the local feature are
overlapped with the input image, as shown in Fig. 2. Thenthe pixel intensities of the input image are emphasized (orweakened) according to the feature values of the same position. The emphasizing image transformation (emphasizing

Phenotype

Genome (Parameter Tunable FFGIN)

No.5

c

No.2

No.6

in1

b

No.1

out1

: connection

out2

e

No.8

No.11

d

g

0

5 0.715 0.283

ﬁlter ID connection① connection②: output node

f

a

e

: inactive node

No.10

No.7

No.4

: image ﬁlter

f

g

No.3

in2

parameter① parameter②: input node
No.9

a

No.0

: inactive connection

Fig. 4. Example of each genome that denotes a list of filter ID, connections,and parameters in parameter tunable FFGIN.

Genotype
No.2
a

No.3
0

0

b

No.8
d

ﬁlter ID

No.4
1

0

e

No.9
4

0

f

No.5
1

c

No.10
6

5

f

connection① connection②

Fig. 3.

2

6

No.6
2

4

g

No.11
7

g

8

No.7
0

3

a

4

6

out1 out2
7

9

10: one input image ﬁlter

Structure of FFGIN (phenotype) and the genotype.

filter) is performed by the following equation.
Oij =

W 
H


Iij Vweight (Fij + Voffset )(1)

i=1 j=1

The weakening image transformation (weakening filter) is
performed by the following equation.
Oij =

W 
H


Iij Vweight ((1.0 − Fij ) + Voffset )(2)

i=1 j=1

where Oij are the pixel values of output image, Iij are thepixel values of input image, and Fij are the normalized
values of local feature among [0.0, 1.0]. The numbers of
pixels in the direction i and j are W , H respectively. Vweightand Voffset are weight value and offset value, respectively.
We use Voffset = 0.5 in this paper. The parameter Vweight
and the window size for calculation of the local features are
optimized in evolutionary process.
This method calculates the local feature of the input image,and transforms it. Although this algorithm is simple, theadaptive image processing filters in which the processingchanges according to the input image can be generated onlywith the preparation of the local features.
In this paper, the local features are calculated by simple statistical values, e.g., mean value, standard deviation,
maximum value, minimum value, mode, 1 sigma in rate, 3sigma out rate, skewness, kurtosis, and so on. The detailsof the local features are described in IV-A). We prepare
emphasizing and weakening filters that are defined Equation(1) and (2) respectively for each local feature.
B. Parameter Tunable Feed Forward Genetic Image Network
1) Structure of FFGIN: FFGIN [13] constructs an acyclic
network-structured image transformation procedure automatically. Fig. 3 shows an example of the phenotype (feedforward network structure) and genotype (string representing
the phenotype) of FFGIN. One of the benefits of this type ofrepresentation is that it allows the implicit reuse of nodes in

its network. The nodes of FFGIN are categorized into three
types: input nodes, image transformation nodes, and output
nodes. Input nodes correspond to original images. Imagetransformation nodes execute image transformation using thecorresponding image processing filters. The output images
are obtained from output nodes. In FFGIN, the feed-forward
network structure of nodes is evolved, as shown in Fig. 3.
Numbers are allocated to each node beforehand. Increasingly
large numbers are allocated, in order, to input nodes, imagetransformation nodes, and output nodes. The feedback structure is restricted at the genotype level. The nodes take their
input from the output of previous nodes in a feed-forwardmanner. Therefore, it is possible to straightforward executionof network structured image transformation.
To adopt an evolutionary method, FFGIN uses genotypephenotype mapping. This genotype-phenotype mapping
method is similar to Cartesian genetic programming (CGP)[23], [24]. The feed-forward network structure is encoded in
the form of a linear string. The genotype in FFGIN is a fixed
length representation. To evolve the parameters of nodes
simultaneously with the structure, we modify the genotype of
FFGIN. We add the genome that denotes the parameters of
nodes to the genotype. The genotype consists of a string that
encodes the node function ID, connections, and parametersof each node in the network. The parameter values lie in[0.0, 1.0]. Fig. 4 illustrates the example of a genome that
denotes a list of filter ID, connections, and parameters in
FFGIN. The number of nodes in the phenotype can vary ina restricted manner, as not all of the nodes encoded in the
genotype have to be connected. This allows the existence ofinactive nodes. In Fig. 3, node No. 8 and 11 are inactive
nodes.
The length of the genotype is fixed and equals to Ntrans (1+nin + npara ) + Nout , where Ntrans is the number of imagetransformation nodes, nin is the maximum number of inputs
of predefined filters, npara is the maximum number of parameters of predefined filters, and Nout is the number of output
nodes. Because FFGIN constructs a feed-forward network
structured image transformation, it can represent multiple
outputs. Therefore, FFGIN enables to construct multipleoutput image transformation using only a single network
structure.
2) Genetic Operators and Generation Alternation Model:
To obtain the optimum structure, an evolutionary method
is adopted. The genotype of FFGIN is a linear string.
Therefore, it is able to use the standard genetic operators.

In this paper, we use uniform crossover and mutation as thegenetic operators. The uniform crossover operator affects two
individuals, as follows:
Select several genes randomly according to the uniform
crossover rate Pc for each gene.
• The selected genes that denote filter ID or connection
are swapped between two parents. The selected genesthat denote the parameter are updated to the value ofrp1 + (1 − r)p2 , where p1 and p2 are the selectedparameters of parents 1 and 2, respectively, r is a
uniformly distributed random number among [0.0, 1.0].
•

The mutation operator affects one individual, as follows:
Select several genes randomly according to the mutation
rate Pm for each gene.
• Change the selected genes randomly under the constraints of the structure.
•

It uses the minimal generation gap (MGG) model [25] asa generation alternation model. The MGG model is a steadystate model proposed by Satoh et al., having a desirable
convergence property of being able to maintain the diversity of the population, and shows higher performance thanother conventional models in a wide range of applications(especially real parameter optimization [26], [27]). The MGG
model in this paper is summarized as follows:
1) Set the generation counter t = 0. Generate N individuals randomly as the initial population P (t).
2) Select a set of two parents M by random samplingfrom the population P (t).
3) Generate a set of m children C by applying thecrossover and the mutation operation to M .
4) Select two individuals from set M + C. One is theelitist individual and the other is the individual fromthe tournament selection. Then replace M with the two
individuals in population P (t) to get population P (t +1).
5) Stop if a certain specified condition is satisfied; otherwise, set t = t + 1 and go to step 2.
The MGG model localizes its selection pressure, not to thewhole population as simple GA or steady state does, but only
to the family (children and parents). In the experiments we
use children size m of 10 and tournament size 5.
IV. E XPERIMENTS AND R ESULTS
FFGIN enables to construct multiple-output image transformation procedures using only a single network structure.
In this section, the image transformation algorithms thatextract multiple regions from the PET (positron emission
tomography) images are constructed by FFGIN. In conventional method, ACTIT, it is necessary to construct the imagetransformation algorithms of each region. Our proposed
method is able to represent multiple image transformation
procedures using a single network. We expect to constructcompact structure by reusing of nodes.

TABLE IP REPARED ADAPTIVE IMAGE PROCESSING FILTERS BASED ON LOCALFEATURES USED IN THE EXPERIMENTS .

MarkEmphasizing
Weakening
WwWM
wM
Wm
wm
WS
wS
WT
wT
WI
wI
WO
wO
WF
wF
WD
wD
WQ
wQ
Wd
wd
Wk
wk
WL
wL

Name of local feature
Mean value
Maximum value
Minimum valueStandard deviation
Maximum value - minimum value
1 sigma in rate
3 sigma out rate
First quartile
MedianThird quartile
Mode
Skewness
Kurtosis
TABLE IIP REPARED ORDINARY IMAGE PROCESSING FILTERS FOR FFGIN USED IN
THE EXPERIMENTS .
MarkName of image processing filter
One-input one-output filters
G2
Gamma correction (γ = 0.2 ∼ 2.2)
l2
Inversion: f → Vmax − f
Two-input one-output filters (f1 : input image 1, f2 : input image 2)
2L
Logical sum: max(f1 , f2 )2l
Logical product: min(f1 , f2 )2AAlgebraic sum: f1 + f2 − f1 · f2 /Vmax
2aAlgebraic product: f1 · f2 /Vmax
2B
Bounded sum: f1 + f2
2bBounded product: f1 + f2 − Vmax
2u
Drastic sum: f1 = 0 → f2 , orf2 = 0 → f1 ,orf1 , f2 = 0 → Vmax
2U
Drastic product: f1 = Vmax → f2 , orf2 = Vmax → f1 ,orf1 , f2 = Vmax → 0
2D
Difference: |f1 − f2 |A. Experimental Settings
The prepared adaptive image processing filters based on
local features used in the experiments are shown in Table
I. There are two filters to each local feature, which areemphasizing and weakening filters. Capital letter “W” ofthe mark in Table I indicates emphasizing filter, and small
letter “w” of the mark in Table I indicates weakening filter.
The parameters of each filter, window size and Vweight , areoptimized simultaneously with the network structure. The
window size takes any of the size of 7 × 7, 9 × 9, 11 × 11,
and 13×13 according to the value of the first parameter gene.
Vweight is a value within the range of [0.3, 1.8] according to
the value of the second parameter gene. Because offset value
Voffset in Equation (1) and (2) is assumed to be 0.5, the pixel
intensities of the input image are multiplied by the value
among [0.15, 2.7] by filtering once.
A part of ordinary image processing filters is also used for
FFGIN. The ordinary image processing filters used in theexperiments are shown in Table II. The parameter of γ ingamma correction is also optimized in evolutionary process.
γ is a value within the range of [0.2, 2.2] according to thefirst value of the parameter gene.
Although we do not prepare binarization filter for FFGIN,TABLE III
PARAMETERS OF FFGIN USED IN THE EXPERIMENTS .
Parameter
Number of generations
Population size
Crossover rate
Uniform crossover rate (Pc )Mutation rate (Pm )Maximum number of image transformation nodes

Value
10000
200
0.7
0.1
0.05
50

the binarization is executed in the output nodes to obtain thebinary output images. The threshold of the binarization isdecided by discriminant analysis.
The parameters of FFGIN are shown in Table III. We use
the mean error on the training images as a fitness function
for FFGIN. The fitness function F used in the experiments
is described as follows:
⎧
⎫
W 
H

⎪
⎪
mn mn
mn ⎪
⎪
wij |oij − tij | ⎪
N
M ⎪
⎬
1 ⎨
i=1 j=1
1−
(3)
F =
W 
H
⎪

M N n=1 m=1 ⎪
⎪
⎪
mn
⎪
⎪
V
w
⎩
⎭
max
ij
i=1 j=1

where omn
ij are the pixel values of transformed images (output
mnimages), tmn
ij are their target ones, and wij are their weight

ones that indicate the important degree of pixel. The numbersof pixels in the direction i and j are W , H, respectively,
the number of output images is M , the number of training
images is N , and the maximum value of each pixel is
Vmax (= 255). The target images are the images that users
require after the image processing (ideal results). The rangeof this fitness function is [0.0, 1.0]. A higher numerical value
indicates better performance.
B. Segmentation of Lungs, Heart, and Body Regions
The training images we used in this experiment appear
in Fig. 5. The training images consist of an original image, target images, and weight images. This original image
is PET (positron emission tomography) image. PET is akind of medical image and indicates the metabolizing of
human body. This technology can determine the presence
and severity of cancers, thus PET is recently becoming amajor diagnostic imaging method. All images used in theexperiments are gray scale images and the size of 128 × 128
pixels. Because of low resolution and unclear indication, it ishard to distinguish borderline of internal organs. We prepared
the target and the weight images manually. The purpose ofthis image transformation is to extract lung regions withweak accumulation, a heart region placed between lungs, andwhole body region respectively. The target and the weight
images 1, 2, and 3 correspond to the regions of lungs, heart,and body, respectively.
The obtained network structured image transformation
procedure, the output images, and the images under transformation are displayed in Fig. 6. From this structure, acommon structure is used for multiple outputs at the earlystage of the processing. Our proposed adaptive filters that

Original image

Target image 1

Target image 2

Target image 3

Weight image 1

Weight image 2

Weight image 3

Fig. 5. Training images in the experiment of segmentation of lungs, heart,and body regions.

use standard deviation and mean value as the local feature
are used at the early stage of the processing for smoothing.
Then it branches after inversion filter (I2) is executed. In thefirst route, it processes in order of “W-” with the window
size of 11 × 11, “WM” with the window size of 11 × 11
(emphasizing the region of lungs and the background), and“wM” with the window size of 13×13 (weakening the regionof the background), and the output image 1 to which only theregion of lungs is extracted is obtained finally. In the secondroute, it processes in order of “w-” with the window size of7 × 7 (smoothing the heart accumulation part mainly), and“WM” with the window size of 9×9 (emphasizing the regionof heart accumulation), and the output image 2 that is only
extracted the region of heart is obtained finally. In the thirdroute, it processes of “w-” with the window size of 7 × 7 forsmoothing the heart accumulation part. The region of wholebody transforms into the same pixel intensities because ofthis processing, and the output image 3 to which the region ofwhole body is extracted is obtained finally. All output images
are similar to the target images, and the fitness value of thisimage transformation is 0.983. In addition, the constructedstructure is compact and reuses the transformed images in
its network.
Next, we apply the constructed network structured imagetransformation procedure to the test images. The test images
used in the experiments are shown in Fig. 7, which are
not used in evolutionary process (the non-training images
that are similar to training images). The number of the test
images is three. The results (the output images) also appear
in Fig. 7. From these output images, the obtained imagetransformation extracts each region correctly and outputs theideal images. It shows that our method constructs generalimage transformation for segmentation from PET images
through learning.
C. Segmentation of Lungs, Cancer, and Body Regions
The training images we used in this experiment appear in
Fig. 8. This PET image is an image of the patient with thelung cancer. Strong accumulation exists in the lungs region
where originally weak accumulation should be shown. Thisﬁlter : wS
windowsize : 13 x 13
weight : 1.320

Target image 1
ﬁlter : 2l

Original image

Target image 2
ﬁlter : Wwindowsize : 11 x 11
weight : 0.503
ﬁlter : I2
ﬁlter : wwindowsize : 7 x 7weight : 1.335

Target image 3

Test image

ﬁlter : Wwindowsize : 11 x 11
weight : 1.151

ﬁlter : WM
windowsize : 11 x 11
weight : 1.728

Output image 1 Output image 2 Output image 3

Fig. 7. Test images that are not used in evolutionary process, and their
output images transformed by the obtained image transformation procedure
for segmentation of lungs, heart, and body regions.
ﬁlter : WM
windowsize : 9 x 9weight : 1.638

ﬁlter : wM
windowsize : 13 x 13
weight : 1.370

Output image 3

Original image

Target image 1

Target image 2

Target image 3

Weight image 1

Weight image 2

Weight image 3

Fitness : 0.983
Output image 1

Output image 2

Fig. 6. Obtained network structured image transformation procedure forsegmentation of lungs, heart, and body regions.

strong accumulation appears from the carcinoma cell. The
purpose of this image transformation is to extract normallung regions, regions with abnormal accumulation, and wholebody region, respectively. The target and the weight images
1, 2, and 3 correspond to the regions of lungs, abnormal
accumulation, and body, respectively.
The obtained network structured image transformation
procedure, the output images, and the images under transformation are displayed in Fig. 9. According to this structure, areusing structure is used at the early stage of the processing
as well as the previous experiment. It processes in order of“wT” with the window size of 15×15 (weakening the regionof abnormal accumulations that have high intensity), andcalculate the drastic product (2U) to obtain the output image
2 that is extracted only abnormal accumulation. To obtainoutput image 1 that is only extracted the region of lungs,
the region of lungs in the inverted image is emphasized with
“W-” with the window size of 9 × 9, then the backgroundis weakened with “wM” with the window size of 15 × 15.
All output images are similar to the target images as well as
the previous experiment, and the fitness value of this imagetransformation is 0.984.
Next, we apply the constructed network structured imagetransformation procedure to the test images. The test images
used in the experiment are shown in Fig. 10. The results

Fig. 8. Training images in the experiment of segmentation of lungs, cancer,
and body regions.

(the output images) also appear in Fig. 10. From these
output images, it succeeds in extraction of the lung cancer
accumulations to which we cannot predict in the lung.
Moreover, it extracts normal lung regions and whole body
region simultaneously.
D. Comparison with Conventional ACTIT
We compare the proposed method to conventional ACTIT
to verify the effectiveness of it. ACTIT constructs tree
structured image processing filters to satisfy the given conversion from original to target images using predefined wellknown filters. Thus, ACTIT only represents one output imagetransformation algorithms. In this experiment, we use only
ordinary image processing filters without using our proposed
adaptive image processing filters. We prepare simple andwell-known image processing filters for ACTIT in the experiment (21 one-input one-output filters and 9 two-input oneoutput filters), e.g., mean filter, maximum filter, minimum
filter, Sobel filter, Laplacian filter, gamma correction filter,binarization, linear transformation, difference, logical sum,
logical prod, and so on.

Original image

Target image 1

Target image 2

Target image 3

ﬁlter : wT
windowsize : 15 x 15
weight : 1.172
ﬁlter : I2
ﬁlter : WM
windowsize : 9 x 9weight : 0.521
ﬁlter : G2 ( γ = 0.596)

ﬁlter : wF
windowsize : 13 x 13
weight : 0.770

Test image
ﬁlter : Wwindowsize : 9 x 9weight : 0.692

ﬁlter : wwindowsize : 13 x 13
weight : 1.760
ﬁlter : 2U

ﬁlter : wM
windowsize : 15 x 15
weight : 0.813

Output image 1 Output image 2 Output image 3

Fig. 10. Test images that are not used in evolutionary process, and their
output images transformed by the obtained image transformation procedure
for segmentation of lungs, cancer, and body regions.

Output image 3

Fitness : 0.984
Output image 1

Output image 2

Fig. 9. Obtained network structured image transformation procedure forsegmentation of lungs, cancer, and body regions.

We apply ACTIT to construction of a segmentation algorithm that extracts the region of lungs from the PET imagesof the normal patient. The training images and their output
images used in this experiment appear in Fig. 11. We prepare
three training images for ACTIT. The output images are
relatively approximated to the target images although theshape of the output image is distorted. Fig. 12 shows the test
images and their output images transformed by the obtained
image transformation procedure by conventional ACTIT.
From these output images, the over-extraction and extraction
shortage of the lungs are observed. The performances of theextraction are worse than our proposed method. This cause
is that the image processing filters prepared in conventional
method cannot consider the local features. Therefore, it
tends to construct the image transformation procedures thatspecialize in the training images. On the other hand, thegeneral effective algorithms for the test image are constructedby our proposed method. In addition, our method constructs
the multiple output image transformation using a single
network.
V. C ONCLUSIONS AND F UTURE W ORK
In this paper, we introduce the adaptive image processing
filters that process according to the features of an input image
to FFGIN. The processing of the adaptive filters is decided
based on the local features of an input image. We implement

Original image

Target image

Weight image

Output image

Fig. 11. Training images and their output images transformed by theobtained image transformation procedure by conventional ACTIT for segmentation of lungs.

them in FFGIN and modify it to be parameter tunable. Then
we apply it to the problems of automatic construction ofsegmentation algorithms of organs and tissues in medical images. It is not possible to simply judge the segmented regions
by the intensity of the pixels in these problems. Experimental
results show that the image transformation procedures withmultiple outputs are constructed, and we confirmed that our
method is effective and general than the conventional method
for medical images. In future work, we have to perform aquantitative analysis about this method using more images.
Then, we will apply the proposed method to other medical
images, such as CT, MRI, and DWI (diffusion weighted
image). Moreover, we will introduce the other features to our
method and construct more effective image transformation
algorithms.

Test images

Output images of conventional ACTIT

Fig. 12. Test images that are not used in evolutionary process, and their
output images transformed by the obtained image transformation procedureby conventional ACTIT for segmentation of lungs.

R EFERENCES[1] J. R. Koza, Genetic Programming: On the Programming of Computers
by Means of Natural Selection. MIT Press, 1992.
[2] R. Poli, W. B. Langdon, and N. F. McPhee, A Field Guide to Genetic
Programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk, 2008, (Withcontributions by J. R. Koza).
[3] W. A. Tackett, “Genetic programming for feature discovery and image
discrimination,” in Proceedings of the 5th International Conference on
Genetic Algorithms (ICGA ’93). Morgan Kaufmann Publishers, 1993,
pp. 303–309.
[4] A. Teller and M. Veloso, “PADO: A new learning architecture forobject recognition,” in Symbolic Visual Learning, K. Ikeuchi and
M. Veloso, Eds. Oxford University Press, 1996, pp. 81–116.
[5] M. Zhang and C. G. Fogelberg, “Genetic programming for image
recognition: An LGP approach,” in Applications of Evolutionary
Computing: EvoWorkshops 2007, ser. LNCS, vol. 4448. SpringerVerlag, 2007, pp. 340–350.
[6] B. Lam and V. Ciesielski, “Discovery of human-competitive image
texture feature extraction programs using genetic programming,” inProceedings of the Genetic and Evolutionary Computation Conference
2004 (GECCO ’04), Part II, ser. LNCS, vol. 3103. Springer-Verlag,
2004, pp. 1114–1125.
[7] M. Aurnhammer, “Evolving texture features by genetic programming,”
in Applications of Evolutionary Computing: EvoWorkshops 2007, ser.
LNCS, vol. 4448. Springer-Verlag, 2007, pp. 351–358.
[8] P. Ghosh and M. Mitchell, “Segmentation of medical images using
a genetic algorithm,” in Proceedings of the Genetic and Evolutionary
Computation Conference 2006 (GECCO ’06). ACM, 2006, pp. 1171–
1178.
[9] T. Kowaliw, W. Banzhaf, N. Kharma, and S. Harding, “Evolving novel
image features using genetic programming-based image transforms,” inProceedings of the 2009 IEEE Congress on Evolutionary Computation(CEC ’09), 2009, pp. 2502–2507.
[10] S. Aoki and T. Nagao, “Automatic construction of tree-structural imagetransformation using genetic programming,” in Proceedings of the1999 International Conference on Image Processing (ICIP ’99), vol. 1.
IEEE, 1999, pp. 529–533.
[11] Y. Nakano and T. Nagao, “3D medical image processing using 3DACTIT; automatic construction of tree-structural image transformation,” in Proceedings of the International Workshop on Advanced
Image Technology 2004 (IWAIT ’04), 2004, pp. 529–533.
[12] S. Shirakawa and T. Nagao, “Genetic image network (GIN): Automatically construction of image processing algorithm,” in Proceedingsof the International Workshop on Advanced Image Technology 2007
(IWAIT ’07), 2007, pp. 643–648.
[13] S. Shirakawa and T. Nagao, “Feed forward genetic image network: Toward efficient automatic construction of image processing algorithm,”
in Advances in Visual Computing, Third International Symposium,
ISVC 2007, Part II, ser. LNCS, vol. 4842. Springer-Verlag, 2007,
pp. 287–297.

[14] Y. Nakano and T. Nagao, “Automatic extraction of internal organs
region from 3D PET image data using 3D-ACTIT,” in Proceedingsof the International Workshop on Advanced Image Technology 2006
(IWAIT ’06), 2006.
[15] Y. Nakano and T. Nagao, “Automatic construction of moving object
segmentation from video images using 3D-ACTIT,” in Proceedingsof the 2007 IEEE International Conference on Systems, Man, andCybernetics (SMC ’07), 2007, pp. 1153–1158.
[16] S. Shirakawa, S. Nakayama, and T. Nagao, “Genetic image network
for image classification,” in Applications of Evolutionary Computing:
EvoWorkshops 2009, ser. LNCS, vol. 5484. Springer-Verlag, 2009,
pp. 395–404.
[17] K. Krawiec and B. Bhanu, “Visual learning by coevolutionary featuresynthesis,” IEEE Transactions on System, Man, and Cybernetics –Part
B, vol. 35, no. 3, pp. 409–425, 2005.
[18] K. Krawiec and B. Bhanu, “Visual learning by evolutionary andcoevolutionary feature synthesis,” IEEE Transactions on Evolutionary
Computation, vol. 11, no. 5, pp. 635–650, 2007.
[19] U. Watchareeruetai, Y. Takeuchi, T. Matsumoto, and N. Ohnishi,“Transformation of redundant representations of linear genetic programming into canonical forms for efficient extraction of image
features,” in Proceedings of the 2008 IEEE World Congress on
Computational Intelligence (WCCI ’08), 2008, pp. 1996–2003.
[20] B. Bhanu and Y. Lin, “Learning composite operators for object detection,” in Proceedings of the Genetic and Evolutionary Computation
Conference 2002 (GECCO ’02). Morgan Kaufmann Publishers, 2002,
pp. 1003–1010.
[21] T. Singh, N. N. Kharma, M. Daoud, and R. Ward, “Genetic programming based image segmentation with applications to biomedical
object detection,” in Proceedings of the Genetic and Evolutionary
Computation Conference 2009 (GECCO ’09). ACM, 2009, pp. 1123–
1130.
[22] S. Colton and P. Torres, “Evolving approximate image filters,” inApplications of Evolutionary Computing: EvoWorkshops 2009, ser.
LNCS, vol. 5484. Springer-Verlag, 2009, pp. 467–477.
[23] J. F. Miller and P. Thomson, “Cartesian genetic programming,” inGenetic Programming: 3rd European Conference, EuroGP 2000, ser.
LNCS, vol. 1802. Springer-Verlag, 2000, pp. 121–132.
[24] J. F. Miller and S. L. Smith, “Redundancy and computational efficiency
in cartesian genetic programming,” IEEE Transactions on Evolutionary
Computation, vol. 10, no. 2, pp. 167–174, 2006.
[25] H. Satoh, M. Yamamura, and S. Kobayashi, “Minimal generation
gap model for considering both exploration and exploitations,” inProceedings of the IIZUKA’96, 1996, pp. 494–497.
[26] S. Tsutsui, M. Yamamura, and T. Higuchi, “Multi-parent recombination with simplex crossover in real coded genetic algorithms,” inProceedings of the Genetic and Evolutionary Computation Conference
1999 (GECCO ’99). Morgan Kaufmann Publishers, 1999, pp. 657–664.
[27] K. Deb, A. Anand, and D. Joshi, “A computationally efficient evolutionary algorithm for real-parameter optimization,” Evolutionary
Computation, vol. 10, no. 4, pp. 371–395, 2002.