International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: 03

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An Application Of Backpropagation Artificial Neural Network Method
for Measuring The Severity of Osteoarthritis
Dian Pratiwi1, Diaz D. Santika2, and Bens Pardamean3
1

Information Engineering Department, Trisakti University, Jakarta, Indonesia
Information Technology Department, Bina Nusantara University, Jakarta, Indonesia
3
Information Technology Department, Bina Nusantara University, Jakarta, Indonesia
2

The examination of Osteoarthritis disease through X-ray by rheumatology can be classified into four grade of severity. This
paper discusses about the application of artificial neural network backpropagation method for measuring the severity of the disease,
where the observed X-ray range from wrist to fingers.
The main procedures of system in this paper is divided into three, which are image processing, feature extraction, and
artificial neural network process. First, an X-ray image digital (200x150 pixels and greyscale) will be thresholded, then extracted
features based on probabilistic values of the color intensity of seven bit quantization result, and statistical textures.
That feature values then will be normalizing to interval [0.1, 0.9], and then the result would be processing on backpropagation
artificial neural network system as input to determine the severity of disease from an X-ray had input before it. From testing with
learning rate 0.3, momentum 0.4, hidden units five pieces and about 132 feature vectors, this system had had a level of accuracy of
100% for learning data, 80% for learning and non-learning data, and 66.6% for non-learning data
Index Terms—Backpropagation, Feature Extraction, Normalization, Osteoarthritis

I.

INTRODUCTION

The disease know as osteoarthritis or calcification generally
occurs in areas around joints such as cartilage and joints the
fingers, knees, and spine that causes symptoms of fatigue,
inflammation, pain and swelling that accompanied reddish
color. These symptoms are used as an initial analysis by
rheumatologist. However, in ensuring conditions of
osteoarthritis not simply on the basis of the symptoms that
arise. Because of many cases showed that advanced stage
osteoarthritis are not or only slightly showing symptoms, and
vice versa. Therefore, further tests such as X-rays, Magnetic
Resonance Imaging (MRI), or Computed Tomography
Imaging (CT Scan) is recommended to do in order to give
more accurate results.
In this paper, we discuss a classification technique based on
Artificial Neural Network (ANN) is applied by the author in
order to help predict or measure the severity of the
osteoarthritis disease through X-ray image from the fourth
grade. The fourth grade have a different description of the
osteoarthritis condition .and only can be diagnosed by
rheumatologist, radiologist, or orthopedist through a series of
medical examinations.
Some images are used by orthopedist or rheumatologist in
determining the severity of osteoarthritis disease from X-ray
photograph is to see whether or not cysts, swelling, formation
of new bone that is not flat and spiky (osteophyte),
subchondral sclerosis, and reducing cartilage mass [1]. The
description of these condition then made it as a reference in
assisting rheumatologist ensure osteoarthritis disease from the
X-ray image set.
Every characteristic and feature of osteoarthritis that appear
on X-ray image will be processed and extracted based on the
brightness (intensity) and texture, which then will be
processed to generate predictions in software-based artificial
neural network backpropagation method.

1. Goal
The goal in this paper, ie :
a) Obtaining a correct prediction results in a measure the
severity of osteoarthritis disease of each X-ray image
b) Generate one way to classify the level of osteoarthritis
disease is by applying the method of backpropagation
artificial neural network.
2. Benefits
The benefits in this paper are :
a) Helps rheumatologist in ensuring the severity of
osteoarthritis from patient’s condition, so that
treatment can be given with no undertreatment or
overtreatment
b) Can be used as a basis of preliminary design or
develop a system of measuring the severity of other
diseases, which can be applied in subsequent studies.
II. SAMPLE AND PROCEDURE
A. Sampel Data
The data used in this study is a set of X-ray images derived
from patients with osteoarthritis with a variety of conditions.
This condition can be divided into four grades of severity with
a different picture, namely [1] :
a. Grade 1 (Doubtful) :
 Distal interphalangeal joints : Normal joint except for
one minimal osteophyte.
 Proximal interphalangeal joints : Minimal osteophytosis
at one point and possible cyst
 First carpometacarpal joint : Minimal osteophytosis and
possible cyst formation
b. Grade 2 (Minimal) :
 Distal interphalangeal joints : Definite osteophytes at
two points with minimal subchondral sclerosis and
doubtful subchondral cysts, but good joint space and no
deformity

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International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: 03
 Proximal interphalangeal joints : Definite osteophytes
at two points and possible narrowing of joint space at
one point
 First carpometacarpal joint : Definite osteophytes and
possible cysts
c. Grade 3 (Moderate) :
 Distal interphalangeal joints : Moderate osteophytes,
some deformity of bone ends and narrowing of joint
space
 Proximal interphalangeal joints : Moderate osteophytes
at many points, deformity of bone ends
 First carpometacarpal joint : Moderate osteophytes,
narrowing of joint space, and subchondral sclerosis and
deformity of bone ends
d. Grade 4 (Severe) :
 Distal interphalangeal joints : Large osteophytes and
deformity of bone ends with loss of joint space,
sclerosis, and cysts
 Proximal interphalangeal joints : Large osteophytes,
marked narrowing of joint space, subchondral sclerosis,
and slight deformity
 First carpometacarpal joint : Large osteophytes, severe
sclerosis, and narrowing of joint space

a)
b)
Fig. 1. X-ray image of hands. (a) X-ray image of nonosteoarthritis (b) X-ray image of osteoarthritis [2]

a)
b)
c)
d)
Fig. 2 [1]. X-ray image of Osteoarthritis in the various grades
of The Distal Interphalangeal joints ; (a) Grade 1, (b) Grade2,
(c) Grade 3, (d) Grade 4

a)
b)
c)
d)
Fig. 3 [1]. X-ray image of Osteoarthritis in the various grades
of The Proximal Interphalangeal joints ; (a) Grade 1, (b) Grade
2, (c) Grade 3, (d) Grade 4

a)

b)

c)

d)

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Fig. 4[1]. X-ray image of Osteoarthritis in the various grades
of The First Carpometacarpal joint ; (a) Grade 1, (b) Grade 2,
(c) Grade 3, (d) Grade 4
X-ray region were observed and ranged from wrist to
fingers with a size of 200x150 pixels. The number of samples
that will be used is 60 x-ray images.
B. Procedure
In this study, there is a set of processes undertaken to
produce a prediction system and the percentage of
measurement accuracy.
1. Image processing
a. Convert RGB color to grayscale
Convert color image of The X-ray done to change the
color of RGB (24 bit) into grayscale (8 bit), so that the gray
value at interval 0 to 255.
b. Thresholding
A process to classify the pixels within the limits of
particular intensity, and separates the image between the
foreground with the background [3]. In this study, the
appropriate threshold value is taken through a process of
trial and error for more or less suitable for all images [4].
2. Feature extraction
Feature extraction is the first step in conducting a
classification and image interpretation. The process associated
with the quantization characteristic of the image into the
appropriate group of feature values. With the feature
extraction, important information in the image can be
captured and stored into a feature vector.
In this study, the extracted features are based on the
intensity of color and texture.
a. Feature extraction based on color intensity
The intensity of color obtained from the simplification of
the three components RGB into one component intensity
with grey level value between 0 – 255. Distribution of
intensity values of an image can be viewed via a
histogram, which the probability of every grey level value
can be taken using the formula :
(1)
Where ni is the number of pixels that have grey level
value i (i = 0... L = 1), L is the maximum interval of color,
n is the total pixels of image, and hi is the probability of i
grey level value.
Grey level values then can be grouped into several levels
through the process of quantization. Quantization will
divide the grayscale [0, L-1] into G levels are expressed
as an integer.
G = 2m
(2)
Where G is the grey level, and m is the positive integer or
the number of bits
b. Feature extraction based on texture
Image feature extraction based on first-order texture can
use statistical methods, namely by looking at the
statistical distribution of grey level on the image
histogram [5]. From the values in the histogram, can be
calculated feature parameters include :
1) Variance (σ2)

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International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: 03
Shows the variation element of the histogram of an
image
(3)
Where fn is the intensity value of grey level, μ is the
average value of grey level intensity, p(fn) is the value
of histogram ( probability of occurence of intensity in
the image)
2) Skewness (α3)
Shows the level of relative inclination of an image
histogram curve
(4)
3) Entropy (H)
Shows the shape of an irregular size image
(5)
4) Relative smoothness (R) [6]
Shows the level of relative smoothness of an image
form.
3.

4.

(6)
Normalization of Feature Vector
Normalization is a method to classify the range or
interval of values that are different to the same scale
and smaller. In this study, Min-Max Normalization is
used as normalization method [7] .
(7)
Where D is the natural data, U and L are the upper and
lower normalization bound.
In this study, the use of normalization will be applied to
classify the values of the different features into the
range between 0.1 to 0.9.
Classification with Backpropagation Neural Network
After pre-processing step performed, the values of the
resulting features will the be processed in The
Backpropagation Neural Network. From this process
will be obtained prediction of disease all the data which
will then determine the percentage of success of this
method.
III. RESULTS AND DISCUSSION

The tests in this study consists of image processing phase,
feature vector extraction phase, and three network phases that
is the network training phase, validation phase, and final
testing phase. In the training phase, x-ray image that is used
as many as 36 pieces from 60% of the total data, with each
level of severity of disease was nine images. While the
validation phase, x-ray are used as much as 20 images, with
each level of severity of disease was five images. And in the
final testing phase, the x-ray that was used as many as 12
images.
A. Image Processing (Pre-Processing)
In this study, image processing phase is done first, ie the
conversion of X-ray color from the RGB color into greyscale.
After that, every X-ray then going through the process of
thresholding to take a detailed framework of the bone area of

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hands (especially the joints) which is needed in diagnosing the
osteoarthritis disease.
Threshold value that is used on the results obtained from the
thresholding process of several X-ray images trial and error
between 30 to 70.
From the test results, use the threshold value of 50 has made
some parts of detail object framework of bone is reducted.
This will cause the number of missing essential information,
so that the results of the osteoarthritis level classification on
the network will be less than the maximum possible. While the
use of threshold of 30, the background is still not a whole
successfully removed and this will cause the pixels will be
considered as a foreground that allows the network will be
difficult to recognize pattern correctly. So that we determined
the threshold value in the thresholding process is equal to 40,
because of the test results showed that these values are best
used in a separate object area with a background and able to
maintain the detail object.
B. Feature Vector Extraction
Feature extraction process that is conducted in this study is
based on the categories of color and texture. Color features is
taken through the calculation of the probability histogram
(interval 0 to 255) which then go through the quantization will
generate the values of the features as much as 128 features.
While on the texture, the feature is taken through the
calculating formula of the value of statistical variance,
skewness, entropy, and relative smoothness. Thus, the overall
number of features used is about 132 features.
The following is Table I that contains some texture feature
values of eight training images (with a total of two X-ray
images of each grade) are extracted.
TABLE I. TEXTURE FEATURE VALUE OF DIFFERENT TRAINING IMAGES

No

Variance

1
2
3
4
5
6
7
8

2542.55
1550.28
3959.754
605.424
3498.11
3799.3
2246.89
997.393

Texture Features
Skewness Entropy
-0.644
-0.968
-1.174
0.128
-1.19
-1.143
-1.196
-0.248

0.704
0.819
0.922
0.495
0.885
0.865
0.987
0.592

Relative
Smoothness
1
0.999
1
0.998
1
1
1
0.999

C. Training Phase
At this phase, all feature vectors that have been generated
from the feature extraction and normalization process will be
trained repeatedly in the network to get the best weight value.
These weights resulting from training that has reached 100%
recognition rate, ie when the Mean Square Error (MSE) value
was smaller then the value of tolerance. These weights then
will be used in the validation and testing phase.
The configuration of neural network parameters which have
given in the training process in this study is three layers (one
input layer, one hidden layer, and one output layer), two units
of output, binary sigmoid activation function, threshold 0.5,
momentum 0.5, learning rate 0.3, tolerance 0.001. This study

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International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: 03
also used Nguyen Widrow method for the weight and bias
initialization.
From the results of experiments on the training phase using
36 images (training data), the value of learning rate of 0.3,
learning rate of 0.5, and five hidden units has resulted 100% of
recognition percentage when the 20th epoch, where each
weight then will be stored for use at validation and final
testing phase.
D. Validation Phase
In this study, the validation phase will be used to determine
how likely level of success and error recognition of this ANN
based tool for measuring the severity of osteoarthritis’s
disease prior to testing. The network process consists of feed
forward only step, which will provide a direct output of data in
the form of predictive learning and non-learning are included.
TABLE II. GRADE OR LEVEL MEASUREMENT RESULTS OF OSTEOARTHRITIS’S
DISEASE IN VALIDATION PHASE

Grade
1
4
0
0
0

Measurement Results
Grade Grade Grade
2
3
4
1
0
0
3
1
1
1
4
0
0
0
5

Rheumatologists
Diagnosis
Grade 1
Grade 2
Grade 3
Grade 4

From Table II can be seen, that the four of 20 x-ray images
which included there’s still have an error prediction. This can
be caused by the part of tested data is new data that have not
been trained, so that there’s still a possibility of error
recognition. However, the percentage of success obtained by
80%, so it can be concluded that these tools are quite capable
for use in predicting the disease severity of osteoarthritis in the
process of final testing.
E. Testing Phase
Same as validation phase, testing phase also just run the
feed forward of testing data using the weights from the
previous training process.
TABLE III. GRADE OR LEVEL MEASUREMENT RESULTS OF
OSTEOARTHRITIS’S DISEASE IN TESTING PHASE

Grade
1
3
0
0
0

Measurement Results
Grade Grade Grade
2
3
4
0
0
0
2
1
0
1
1
1
0
1
2

Rheumatologists
Diagnosis

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value is still quite good. And this method can be considered
quite successful in determining the disease severity of
osteoarthritis. Because the amount of diagnostic accuracy
percentage is still higher than the total percentage of failures.
IV. CONCLUSSION
The several of conclusion which can be drawn from this
study ie :
1. The use of backpropagation artificial neural network
method proved to be as one method to classify or
predict the disease severity of osteoarthritis based on
color and texture features with accuracy percentage at
66.6%
2. Determination of the learning rate value at 0.3,
momentum 0.5, five hidden units, and 132 feature
vectors can be stated that the system has been
successfully predict the learning data and less
successful for non-learning data.
REFERENCES
[1] K.D.
Brandt,
M.
Doherty,
M.
Lohmander,
“Osteoarthritis“, Definition and Classification of
Osteoarhtritis, 2nd ed. Oxford University Press, New
York, 2003, pp 1-8
[2] M. Szenroi, “Orthopedics”, Publisher Semmelweis,
Budapest, 2008, pp. 319-328
[3] U. Ahmad, Pengolahan Citra Digital dan Teknik
Pemrogramannya, Penerbit Graha Ilmu, Yogyakarta,
2005
[4] H. Nugroho, “Pengenalan Wajah dengan Jaringan Syaraf
Tiruan Backpropagation”, Proc. of SNATI, 2005, F-59 –
F62
[5] J.S.J. Wong, T. Zrimec, ”Classification of Lung Disease
Pattern using Seeded Region Growing, ” Lecture Notes in
Artificial Intelligence : Proceedings 19th Australian Joint
Conference on Artificial Intelligence, Springer-Verlag,
Germany, 2006, pp. 233-242
[6] B. Kr. Singh, B. Mazumdar, ”Content Retrieval from Xray Images using Color & Texture Features,”
International Journal of Electronics Engineering, 2010,
pp. 25-28
[7] A. Shawkat, K.A. Smith-Miles, “Improved Support
Vector Machine Generalization using Normalized Input
Space,” LNAI, Springer-Verlag, Heidelberg, 2006,
pp.362-371

Grade 1
Grade 2
Grade 3
Grade 4

From Table III above can be seen that the final testing of 12
x-ray images there’s still have error prediction, ie the number
of four images (33.3%). This can be caused by the weights of
the training has not adequately represent the characteristics of
each level of the osteoarthritis disease. So when applied to the
x-ray images that has not been tested or trained (non-learning),
error prediction still can occur. However, if it seen from the
results of measurement accuracy percentage at 66.6%, the
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