Detecting and Determining the Types of Hand
Bone Fracture Using K-Means Clustering
Mohammad Badrul Alam Miah and Afroza Akter*
Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University,
Afroza Akter, Department of Information And Communication Technology, Mawlana Bhashani Science and Technology
University, Santosh-1902, Dhaka, Bangladesh, E-mail:
Received: July 31, 2018; Accepted: August 23, 2018; Published: September 20, 2018
Akter A, Badrul Alam Miah MD (2018) Detecting and Determining the Types of Hand Bone Fracture Using K-Means Page Clustering. J Comp Sci Appl Inform Technol. 3(3): 1-10. DOI: 10.15226/2474-9257/3/3/00136
The purpose of the proposed system is to detect and classifying
the types of human hand fingers bone fracture.
As fracture is very well known to all so at first the x-ray images
has been taken from Atlanta orthopedic institute, Dallas orthopedic
trauma institute, De Claire Lamacchia orthopedic institute, Oliba
orthopedic institute of the Bay Area. After acquiring the images image
preprocessing steps have been done by noise reduction and turning
into binary images with removing unwanted portions. Then k-means
clustering algorithm has been applied for clustering images into six
clusters as it gives a single level of clusters from large amount of data.
Feature extraction method such as moment feature, GLCM feature,
entropy, major axis length, minor axis length, eccentricity, orientation,
convex area, area, filled area, equivalent diameter, solidity, extent,
perimeter, mean, standard deviation, correlation coefficient, median,
variance, height, width, pixel count, Euclidian distance has been used
for classifying the fractures into four types using Artificial Neural
Keywords: Human hand fracture x-ray images; K-means
clustering; GLCM; Moment feature; ANN (Artificial Neural Network);
Comparison to other organs hands bone is the most important
part of our body. Without this human is helpless. Mostly during
the rainy and the winter season when after the rain and snow falls
the roads become useless for walking. Especially for children and
older people it becomes dangerous . Having these various
kinds of accidents like other organs hands bone fracture is very
A hand fracture is a break in one of the bones in your hand.
This includes the bones in the wrist and fingers, and those that
connect the wrist to the fingers. A hand fracture may be caused
by twisting or bending the hand in the wrong way. It may also be
caused by a fall, a crush injury, or a sports injury. The following
figure 1 is about the bones of the human hand .
Figure 1: Bones of the human hand [“Bones in the Hand - Human Body
Pictures & Diagrams - Science for Kids”]
The symbols and prefix of a finger fracture are:
• Labor or emotionalism
• Lump or pounding
• Feeling pathetic in passing one’s hand
• Unusual bump or unusual shape of one’s hand
• Knuckle bone looks submerged in
Types of finger fractures
i. Distal Phalanx Fractures – common type of distal fracture is
tuft fracture. This fracture at the fingertip is associated with a
crush injury. In the following figure 2 the arrow sign signifies
the tuft fracture as the common fracture of distal phalanx
Figure 2: Radiograph of tuft fracture [“afp20120415p805-f3.jpg (275×353)”]
ii. Mallet Fracture –Mallet fractures occur at the insertion of the
terminal finger. It has been caused by an axial load to the tip of
an extended finger. The following figure 3 shows the fracture
where the bone is fragmented at the dorsal surface of the
proximal distal phalanx.
Figure 3: Mallet fracture [“afp20120415p805-f4.jpg (350×149)”]
iii. Flexor Digitorum Profundus Avulsion Fractures–An avulsion
fracture causes from forced hyperextensions of a flexed DIP
joint. The following figure 4 shows the fracture where the
bone is fragmented at the volar surface of the proximal distal
Figure 4: Flexor digitorum profundus avulsion fracture [“afp20120415p805-
iv. Middle and Proximal Phalanx Fractures–These fractures are
combined with trauma. The fractures are classified into intra
or extra articular where the first is complicated and others
are dislocated or located. The following figure 5 shows the
proximal phalanx fracture of fingers.
Figure 5: Proximal phalanx fracture [“Proximal Phalanx Fracture | The
Hand Treatment Center - New Jersey/New York Hand Surgeon”]
In this section the related and the limitations of the proposed
system will be discussed. Here the works is about hand fracture
detection using neural network. Firstly introduce to Computer
aided diagnosis which is the most effective technique for research
area where the systems are used for development .
There are different types of medical imaging tools are
available to detecting different types of problems as X-ray,
Computed Tomography (CT) and Magnetic Resonance Imaging
(MRI) etc. But for bone fracture detection X-ray and CT images are
most frequently used. Generally doctors prefer the X-ray images
to detect the fracture and where it is . Though there are few
limitations in X-ray but for low cost, high speed and usability it is
very effective .
In previous, various bone fracture detection technique is
introduced as Active Contour model (ACM and GACM), Wavelet
and Haar, Support Vector Machine (SVM) Classifier, X-ray/CT auto
classification of fracture (GLCM), Novel morphological gradient
based edge detection technique and can be used segmentation
or fuzzy intelligence [13,15,16,17] etc. The bone fracture is very
natural in old and child ages. The normal and abnormal images
have been found and it has been introduced in .
In , the GLCM (Gray Level Co-occurrence Matrix) approach
has been used to segment the x-ray images of the hand and divides
those separate bands . After dividing the K-means clustering
is used for GLCM texture analysis.
As the same way the GLCM approach has been used in 
where it detects if the fracture in femur exists or not. Here in
the preprocessing steps the images has been convert it into
binary images and after applying edge detection technique the
GLCM based methods used for feature extraction and perform
Another segmentation approach used for x-images of hands
in  as bottom-up region merging method and also compute
combinations between local, regional, global and hierarchical
In , the authors proposed an adaptive interface system
called AdAgen that collaborates with trained agent. Here used
neural network to detect fracture in long bones and must be
mentioned that their simulation result show that how NN
perform detection of fracture in leg radiograph.
The most effective comparison of x-ray image segmentation
techniques has been introduced in . The techniques are
thresholding, region-based methods, edge-based segmentation
methods, clustering or cluster analysis, classification based
segmentation techniques, level set methods, Active contour
models, Active shape models, and Wavelet based techniques and
knowledge based techniques. Among them thresholding, edge
detection, classification based techniques can solve simple image
segmentation problems but for complex active contour models
and active shape models can be used.
On bone fracture detection there has a work  where
fracture has been detected on measuring the neck shaft angle of
the femur. On top of that [8-10] the authors suggest to use Gabor,
markov random field and gradient intensity features and feed
them into SVM (Support Vector Machine). They also show that
these three features improve the accuracy of the model.
The system used in  described how the carpal bones can
be extracted using automatic segmentation methods. In  two
processes has been used for determining skeletal age. One is
image preprocessing using diffusion filter and another is image
segmentation using region level.
Another approaches discussed in [5,13] where authors
proposed to compute the joint width in the x-ray images of hands.
In  proposed a fusion classification technique for detecting
the fracture in tibia bone in x-ray images.
Materials and Methods
In this section the proposed methods has been placed. As
discussed earlier at first images has been collected then image
preprocessing steps has been done, then GLCM and moment
feature has been extracted then classify using ANN. The research
system architecture for overall network is introduced in figure 6
as : (Figure 7,8)
Figure 6: System architecture of detecting X-ray images
Figure 7: Image preprocessing
Figure 8: Image preprocessing
Which method is used for performing operation that can
recognize the images with features is called feature extraction.
It works with a large set of data or value and gives a standard
combination without any difficulty. In this paper GLCM feature,
Moment feature has been extracted.
a) GLCM feature extraction(1-4)
Gray Level Co-occurrence Matrix is a method for extracting
the second order features. It requires a large number of matrices
which provides accuracy for image estimation. It also represents
the specified spatial relationship between pixels [1,2,19].
The system use GLCM to get effective texture from images.
graycomatrix function as it used to scalling and to reduce the
number of intensity value in an image to eight. Where the syntax
can be :
glcm = graycomatrix(i)
But to derive several statistics form graycoprops function is
used for providing information about the texture of an image.
Where the syntax can be:
stats = graycoprops(glcm, properties)
stats is a structure with fields that are specified by properties.
graycoprops functions provide information as:
I. Contrast: Returns the intensity contrast between a pixel and its
neighbor over the inter image.
II. Correlation: Returns the measurement of how correlated
a pixel is to its neighbor over the inter image. Range = [-1 1];
If Correlation is 1 positive relation if correlation -1 negative
relation if correlation is 0 there is no relation between pixel of
III. Energy: Returns the sum of squared elements in the GLCM.
Also known as Angular Second Moment (ASM).
IV. Homogeneity: Also known as (Inverse Difference Moment)
.Returns a value that measures the closeness of the distribution
of elements in the GLCM to the GLCM diagonal.
b) Moment Feature Extraction(5-11)
Calculate the central moments of all orders.
SIGMA = moment(X,ORDER) returns the ORDER-th central
sample moment of the values in X. For vector input, SIGMA is
For a matrix input, moment(X,ORDER) returns a row vector
containing the central moment of each column of X. For N-D
arrays, moment operates along the first non-singleton dimension.
Moment(X,ORDER,DIM) takes the moment along dimension DIM
of X [19,20].
The first central moment is exactly zero. The second central
moment is the variance, using a divisor of N instead of N-1, where
N is the sample size.
The seven moments are computed by normalizing central
moments with order three.
c) Entropy (12)
Image entropy is the amount of information which must be
coded by a compression algorithm. A perfectly flat image will
have zero entropy. It has been computed as:
d) Major axis length (13)
Major axis length returns the length (in pixels) of the major
axis of the ellipse that has the same second-moments as the
region. It is calculated as following:
Major axis = a+b (13)
Where a, b are the distances from each focus to any point on
Minor axis length (14)
Minor axis length returns the length (in pixels) of the minor
axis of the ellipse that has the same second-moments as the
region. It is measured as:
Where f is distance between focus and a, b are the distances
from each focus to any point on the ellipse.
f) Eccentricity (15)
Eccentricity returns the eccentricity of the ellipse that has the
same second-moments as the region. The eccentricity is the ratio
of the distance between the foci of the ellipse and its major axis
length. The value is between 0 and 1. (0 and 1 are degenerate
cases; an ellipse whose eccentricity is 0 is actually a circle, while
an ellipse whose eccentricity is 1 is a line segment.)
g) Orientation (16)
Orientation returns the angle (in degrees) between the
x-axis and the major axis of the ellipse that has the same secondmoments
as the region.
h) Convex area (17)
Convex area returns a scalar that returns the number of pixels
in convex image.
i) Area (18)
Area returns the actual number of pixels in the region. It can
be calculated as:
Where nnz defines the number of nonzero matrix elements.
j) Filled area (19)
It returns the number of pixels in filled image.
k) Equiv diameter (20)
Equiv diameter returns a scalar which gives the diameter of a
circle with the same area as the region. It is measured as:
l) Solidity (21)
It returns a scalar that gives the proportion of the pixels in
the convex hull that are also in the region. It can be measured as:
m) Extent (22)
Extent gives a scalar of the proportion of the pixels in the
bounding box that are also in the region. It is measured as:
n) Perimeter (23)
The perimeter calculates the distance between each adjoining
pair of pixels around the border of the region. If the image
contains discontinuous regions, it returns unexpected results. It
is measured as:
o) Mean (24)
Mean is the average of sum of all the values in the image
matrix [19,23]. It can be calculated as:
p) Standard Deviation (25)
Standard deviation is measures of how spread out a
distribution is. The variance is computed as the average squared
deviation of each number from its mean. Standard deviation is
the square root of variance. It has been computed as:
a) Correlation coefficient (26)
It returns the value of correlation coefficient between two
matrices or vectors of the same size.
b) Median (27)
It determines the median of the gray scale image
c) Variance (28)
This block calculates variance of the input pixels using the
d) We also take ratio as for feature (29-30).
e) Pixel Count (31)
It gives the percentage of black pixel value of the images that
has been calculated as features.
f) Euclidian Distance (32)
The Euclidean distance is commonly used for similarity
measurement in image retrieval due to its efficiency. It counts the
distance between two vectors of images by computing the square
root of the sum of the squared absolute differences . It can be
calculated as follows:
In this section, the experimental result has been shown. At
first the proposed system has been introduced as [19,20], then the
Table 1: Parameter Setting
following figures and tables show the results of the experiment.
For parameter setting the following value is: Table 1
To detect hand fingers fracture images the matlab tool has
been used as it is the high efficient language for x-ray images
. For classification Artificial Neural Network (ANN) with back
propagation technique has been used. The system prefers this
because it subtracts the training output from the target (desired
answer) to obtain the error signal. It then goes BACK to adjust
the weights and biases in the input and hidden layers to reduce
The system used:
Training Class: 02
Targets: 0.4 for Class-1: Normal
Targets: 0.9 for Class-2: Fracture
The following figures figure 9, 10, 11, 12, 13 are the screenshot
of nntraintool, performance, training state and regression of the
ANN.A well trained ANN should have a very low MSE (Mean
Square Error) at the end of the training phase which is measured
in the figure of performance plot.
Figure 9: Proposed network design
Figure 10: Training neural network
Figure 11: Performance of the Network
Figure 12: Training State of the network
Figure 13: Regression of the network
Plotregression (targets, outputs); plots the linear regression
of targets relative to outputs.
Plottrainstate (tr); plots the training state from a training
record tr returned by train. (Table 2, 3, 4)
Table 2: Training with known fracture hand x-ray images
Number of image
Table 3: Training with known normal hand x-ray images
Number of image
Table 4: Compare with existing System
 Al-Ayyoub M, Al-Zghool D. Determining the type of long bone fractures in x-ray images. WSEAS Transactions on Information Science and Applications. 2013;10(8);261-270.
Determining the type of long bone fractures in x-ray images
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Detecting Hand Bone Fractures in X-Ray Images
In conclusion, Using SVM classifier in classification and
testing phase the overall accuracy is more than 85% but using
Artificial Neural Network with back propagation technique in the
testing phase of this system the overall accuracy is 92.24% .
Though the system do not find the types of hand bone fracture
but it can correctly identify if fracture exists if or not. To overcome
this limitation we will work about it in future and also give the
treatment of fractures.
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