=Paper=
{{Paper
|id=Vol-2062/paper11
|storemode=property
|title=Application of Structural and Textural Features from X-ray Images to Predict the Type of Bone Fracture Treatment
|pdfUrl=https://ceur-ws.org/Vol-2062/paper11.pdf
|volume=Vol-2062
|authors=Anam Haq,Szymon Wilk
|dblpUrl=https://dblp.org/rec/conf/dolap/HaqW18
}}
==Application of Structural and Textural Features from X-ray Images to Predict the Type of Bone Fracture Treatment==
https://ceur-ws.org/Vol-2062/paper11.pdf
Application of structural and textural features from X-ray
images to predict the type of bone fracture treatment
Anam Haq Szymon Wilk
Poznan University of Technology Poznan University of Technology
Poznan, Poland Poznan, Poland
anam.haq@put.poznan.pl szymon.wilk@cs.put.poznan.pl
ABSTRACT decides how it should be treated properly. In order to examine
Analysis of medical images plays a very important role in clinical bone fractures various medical imaging technologies are available
decision making. For a long time it has required extensive involve- – they include X-ray and CT (computed tomography) imaging,
ment of a human expert. However, recent progress in data mining with the former being most commonly and widely used for bone
techniques, especially in machine learning, allows for creating examination. The process of manual examination of an X-ray
decision models and support systems that help to automatize this image is very time consuming and tedious, therefore, physicians
task and provide clinicians with patient-specific therapeutic and often make mistakes while inspecting such images [7]. These
diagnostic suggestions. In this paper, we describe a study aimed at mistakes may result in inadequate treatment, like unnecessary
building a decision model (a classifier) that would predict the type surgeries. Several studies have shown that surgery is not needed
of treatment (surgical vs. non-surgical) for patients with bone in every case [6]. Moreover, surgical treatment is not only more
fractures based on their X-ray images. We consider two types of expensive than non-invasive one, but it is also more painful.
features extracted from images (structural and textural) and used This problem can be addressed by building computer-aided
them to construct multiple classifiers that are later evaluated in diagnostic (CAD) tools that automatically identify the presence
a computational experiment. Structural features are computed and type (severity) of bone fracture, and then suggest the most
by applying the Hough transform, while textural information is appropriate treatment for a given patient. However, we have to
obtained from gray-level occurrence matrix (GLCM). In research keep it in mind that human skeleton consist of different types
reported by other authors structural and textural features were of bones (short, long, flat, irregular, and sesamoid) [9], therefore
typically considered separately. Our findings show that while designing a decision model or a CAD system that would deal
structural features have better predictive capabilities, they can with any fracture is a significant challenge [7]. The reason behind
benefit from combining them with textural ones. Interestingly, is that every type of bone requires a different type processing
there are no statistical differences in overall classification accu- workflow involving specialized image analysis algorithms. Be-
racy attained by the classifiers considered in the study (it ranges cause of the difficulty related to this problem intense research
from 91.0% to 96.1%), however, the most promising one is the has been being conducted in the automated fracture detection
random forest. and still there is room for improvement. In this paper we limit the
scope of the problem by providing support only for the decision
KEYWORDS related to treatment and by focusing on long bones (arm and leg)
and upper pelvic bones.
clinical data, X-ray images, classification models, decision sup-
Clinical decision models that rely on images are largely depen-
port
dent on segmentation and feature extraction algorithms. More-
over, building any decision model requires medical domain knowl-
1 INTRODUCTION edge related to the underlying problem [4]. For example, when
detecting a brain tumor in an MRI (magnetic resonance imag-
Over the last decades medical image processing has made sub-
ing) scan it is important to have information about the nature of
stantial progress and has attracted attention from researchers
the tumor. This domain knowledge is helpful when developing
belonging to various fields, e.g., mathematics, computer science,
automated approaches for detection of abnormalities and their
engineering, physics, biology and medicine [10]. Information
further diagnosis [12]. Such abnormalities can be defined by by
systems that store and process image information (e.g., PACS
their structural characteristic (e.g., area, thickness, or thinness) or
– picture archiving and communication systems) have become
by their textural features (e.g., maximum intensity value, energy,
an important component of health IT infrastructure and they
contrast). The use of the Hough transform has been proved useful
are regularly used throughout the patient management process.
in detecting fracture bones [4] as it incorporates the structural
Moreover, development of various image modalities has resulted
details of the bones. To perform texture analysis of the bones
in challenges associated with their efficient processing and ad-
famous gray-level co-occurrence matrix (GLCM) is widely used,
vanced analysis, also in combination with other available types
which was introduced by Haralick et al. [5]. This technique is
of information. The later is often referred to as data fusion [4]. In
based on the assumption that image texture consists of different
this paper we focus on images obtained from a single modality –
regions or sub-regions defined by the characteristics like bright-
X-ray – that represent bone fractures.
ness, color, energy, etc., and that information about these regions
Bone fractures constitute the most common type of injury
may be very useful in image analysis.
that occurs in clinical practice. Normally, during the examination
Recently, application of deep learning methods (e.g., convolu-
process the physician identifies the fracture and its type, and then
tional neural network) to medical imaging problems has gained a
© 2018 Copyright held by the owner/author(s). Published in the Workshop lot of attention [17]. In many problems deep learning has proven
Proceedings of the EDBT/ICDT 2018 Joint Conference (March 26, 2018, Vienna, to be more efficient then tradition image processing techniques
Austria) on CEUR-WS.org (ISSN 1613-0073). Distribution of this paper is permitted
under the terms of the Creative Commons license CC-by-nc-nd 4.0.
and has raised a question regarding the importance of feature ex- 2.3 Feature Extraction
traction among researchers [17]. However, the main problem with Chan et al. in [2] uses three different types of transformations
deep learning is that it requires huge amount of data for learning for feature selection i.e., curvelets, wavelets and Haar. Haar per-
(for example, a learning set considered in the recent competition formed best as compared to wavelet and curvelet transformation.
considered by Kaggle on analyzing fundus images contains tens Aishwariya et al. [13] proposed to use Sobel for detection of bone
of thousands of images), and also does provides no insights into boundaries, and then uses GLCM features to further detect the
its "internals", including the discovered knowledge[17]. presence of bone fractures. This approach was tested on X-ray
As already mentioned above, in this paper we deal with X-ray images and an accuracy of 85% was achieved. However, the most
images of fractured bones. We apply image processing (in particu- difficult task was the segmentation of bone boundaries. Myint et
lar feature extraction) techniques and machine learning methods al. in [11] proposed an algorithm that used edge detection and
to build a decision model that would predict an appropriate type the Hough transformation to automatically detect fracture. The
of treatment (surgical vs. non-surgical) a given patient should un- authors reported that their approach works relatively better on
dergo. Currently, different image processing techniques are used high resolution images. We also used the Hough transform in our
to analyze X-ray images of different types of bones. While the earlier study [4] where it was applied to extract structural fea-
majority of proposed methods focus on a single type of features tures from X-ray images. These features were further fused with
(either structural or textural) for the identification or classifi- non-image data coming from patient record in order to develop
cation of fractures, we consider both types of features. In this a therapeutic model.
way we are able to evaluate their impact on the performance
of resulting decision models (classifiers) and potential benefits
resulting from their synergy. 3 PROPOSED APPROACH
As already discussed, physicians make the decision regarding
the treatment (surgical vs. non-surgical) of patients with bone
2 RELATED WORK fractures by manually examining their X-ray images which is a
tedious process and hence error prone. Our goal is to respond
Work related to our research comes from the three following
to this challenge by constructing a decision model that would
areas: (1) pre-processing of X-ray images (especially for noise
support physicians while making making such decisions.
removal), (2) segmenting bones in these images and (3) extracting
An outline of our approach to feature selection and classi-
features from images. Relevant research work is discussed below:
fier construction is given in Figure 3. We considered different
approaches for pre-processing of X-ray images (see their de-
scription in [7]) and selected the one that is most perceptive in
2.1 Pre-processing detecting bone edges (visually) for the data set at hand. It starts
Vijaykumar et al. [15] proposed an algorithm to to remove Gauss- with pre-processing of an X-ray image by applying a median
ian noise present in X-ray images. Their algorithm estimates the filter (window size 3x3) for noise removal and contrast enhance-
presence of noise in image and replace the value of pixels located ment for amplifying bone edges. Then, two parallel branches are
in the center by mean value of the neighboring pixels based on initiated – the first one is responsible for extracting structural
the threshold value. The proposed filtering algorithm proved to features, and the second one establishes textural features. Both
work better on X-ray images as compared to other filtering ap- branches employ several specific image processing techniques
proaches like Wiener, k-means and bilateral-trilateral algorithms. and are described in details below. Once values of features have
Another approach for noise removal was presented by Al-Khaffaf been obtained, they are merged in a single feature vector – all
et al. [7] where they used k-fill algorithm (calculating the number considered features are listed in Table 1. This vector is finally
of black and white pixels in a filter window of 3x3) to eliminate fed into the learning and classification block where a specific
salt and pepper noise. Moreover, Anu et al. [7] used Gaussian classifier is constructed and then applied to new objects (X-ray
filter of size 3x3 to remove the noise when detecting bone frac- images also characterized by values of extracted features).
ture in X-ray images. Finally, Chai et al. [1] used Laplacian filters
to remove noise from the X-ray while developing algorithm for 3.1 Extraction of Structural features
fracture detection by the help of textural features (GLCM).
We use the Hough transform [11] to extract structural features.
This process consists of the following steps (please refer to [4]
for its illustration):
2.2 Bone Segmentation
In order to detect the boundaries of an object present in a noisy X- (1) The Canny operator is applied to a pre-processed image
ray image Aishwariya et al. [13] proposed an approach that starts to detect edges. Moreover, disconnected components are
with edge detection using Canny edge detection algorithm, and removed from the resulting image,
then applies boundary detection techniques like active contour (2) The Hough transform is applied to detect the bone fracture
model or geodesic active contour model. Smith et al. [14] devel- – the process is explained in detail in [11]. Parameters of
oped a method to detect fractures of pelvic bones. The method the transform are set in such a way that it produces two
uses discrete wavelet transformation for automated segmentation peaks for minor fractures and more then two peak values
of the bone boundary. The wavelet transformation is followed for major fractures.
by a sequence of morphological operations – if at the end as a
result a single boundary is detected, this indicates no fracture. 3.2 Extraction of Textural Features
On the other hand, if multiple boundaries are detected, then this Extraction of textural features employs the GLCM transformation
signals one or more fractures. [1]. The required steps are as follows (see also Figure 2):
Figure 1: Outline of the proposed approach
Table 1: Description of features (S – structural feature, T – textural feature)
Feature Type Description
Hough peak - mean S Mean peak value of the Hough transform.
Hough peak - stdev S Standard deviation of peak values of the Hough transform.
Contrast T Measure of the intensity contrast between the image pixel and its neigh-
bor over the selected ROI, the value of contrast is 0 for a constant image
region.
Energy T Measure of sum of square of elements present in gray-level co-
occurrence vector. The value of energy is 1 for a constant image. It
is also known as uniformity of energy and angular second moment.
Homogeneity T Measure of the degree of closeness between values in the gray-level co-
occurrence matrix. The value of homogeneity is 1 for diagonal GLCM
vector.
Correlation T Measure the degree of correlation i.e., how the value of a pixel is cor-
related over the selected region. Value of correlation is 1 for positive
image and -1 for the negative image.
(1) Region of interest (ROI) corresponding to the fracture is machine learning methods) offered by both tools. This imple-
segmented manually from the pre-processed image, mentation was applied to a set of X-ray images coming from
(2) Laplacian filtering is applied to detect bone boundaries, the data repository provided by the Wielkopolska Center of
(3) The GLCM vector is calculated (specifically, it is obtained Telemedicine (https://www.telemedycyna.wlkp.pl)– a telecon-
by identifying the number of times the pixel i occurred in sultation platform for patients with multiple injuries. The repos-
a spatial relationship with pixel j), then the textural infor- itory includes data of 2030 patients with bone fractures – 1593
mation like contrast, homogeneity, energy and correlation (78.5%) underwent a surgery, and the remaining 437 (21.5%) were
of an input image is obtained from this 2-Dimensional treated non-surgically. Each patient has a clinical record with
vector. non-image data (basic demographics, results of laboratory tests)
and a set of 2-5 X-ray images showing fractures at different stages
4 COMPUTATIONAL EXPERIMENT of treatment. From this repository we randomly selected 210 pa-
tients – 76 (36.2%) non-surgical and 134 (63.8%) surgical cases.
4.1 Experimental Design We changed the distribution of classes to make resulting clas-
We implemented our approach using MATLAB (image process- sifiers less biased towards the surgical class, and the obtained
ing) and WEKA (learning and classification) [16], thus combin-
ing advantages (wide choice of powerful image processing and
Figure 2: Extraction of textural features from an X-ray image: (1) pre-processing, (2) ROI segmentation, (3) application of
Laplacian filter
distribution was established based on suggestions from [3]. More- The most important observations from Table 2 are the follow-
over, for each patient we manually selected a single X-ray image ing:
representing a fractured bone at the time when the management
(1) Classifiers using the structural features (mean and stan-
was initiated.
dard deviation of peak values obtained using Hough trans-
We obtained values of structural and textural features for each
form) were more accurate than classifiers based on the
of the selected images and stored resulting feature vectors in an
textural features (obtained from GLCM). The overall ac-
intermediary ARFF data file for further processing in WEKA. In
curacy obtained by all classifiers based on the structural
fact, we created three versions of this data file to facilitate subse-
features exceeded 90% for all of the considered classifiers.
quent learning: (1) with structural features only, (2) with textural
(2) While the textural features alone resulted in the worse
features only and (3) with all features. Extraction of features
performance for each of the considered classifiers, their
was performed in MATLAB on a MacBook Pro computer with i5
combination with the structural feature always improved
2.7GHz processor and 8GB of RAM and it took 5.23 minutes to
overall classification accuracy. In fact for all considered
complete.
classifiers, the best overall accuracy was achieved when
We then constructed multiple decision models using avail-
using both structural and textural features. A similar ob-
able data. Specifically, we considered the following classifiers
servation was made for accuracies in specific classes –
(in brackets with give their symbols used further in the text): a
the only exceptions were C45 that was more accurate for
k-nearest neighbor classifier with k = 7 (7NN), a naive Bayes
the non-surgical class when using structural features, and
classifier (NB), a tree-based classifier induced with the C4.5 al-
SVM that demonstrated the same performance for struc-
gorithm (C45), a rule-based classifier induced using the RIPPER
tural and all combined features.
algorithm (RIP), a random forest classifier (RF), an SVM classifier
(3) The highest overall accuracy (96.1%) was achieved using
(SVM) and a multilayer perceptron classifier (MLP). Most classi-
RF. It also demonstrated the highest accuracy for the sur-
fiers were generated using default settings in WEKA (for a more
gical class (99.0%). These results confirm the usefulness
detailed description of corresponding learning algorithms [16]) –
of ensemble classifiers, in particular RF, in the task of
only in NB we used supervised discretization, for SVM we used
classifying X-ray images reported by other authors [8].
cost equal to 1e+6 and a radial basis kernel function with gamma
equal to 0.01, and finally for MLP we specified 3 hidden layers. In order to get better insight into captured classification knowl-
Such parameters were established during a preliminary evalua- edge and thus to enhance explanatory capabilities of our ap-
tion. Here we should also note that we built three versions of each proach we analyzed the importance of features as perceived by
classifier – using structural, textural and all features respectively. specific classifiers. Here we focused on classifiers that are capable
While we are aware that building such complex decision models of assessing the importance of specific features and considered
as RF or MLP using only two structural features may be ques- C45, RIP and RF. In C45 more important features appear higher
tionable, we did it to maintain consistency of the experimental in the tree, for RIP such features appear more frequently and in
design. stronger rules (i.e., rules with a larger support), and for RF the
Classification performance of all classifiers was evaluated in 10 importance of features is captured by their weights.
runs of 10-fold cross validation (for better stability of the results) According to RIP the most important attributes are structural
and we use classification accuracy, overall and for both decision features used in combination with energy from textural features
classes (surgical and non-surgical). Computations in WEKA were (see obtained RIP rules in Figure 3). The C45 model gave more
run on the same MacBook Pro as the first part of the experiment importance to structural features and used them in combination
(feature extraction) and it took 10.15 minutes to complete them. with correlation from textural features (see Figure 4). RF assign
weights to features showing the most important ones at the top
4.2 Results which are standard deviation and mean of peak values from the
Classification performance of specific classifiers is given in Table Hough transform followed by a sequence of textural features –
2 where we report overall accuracy along with accuracy values for contrast, energy, correlation and homogeneity (see Fig. 5).
both decision classes. The best results obtained for each classifier We repeated an experiment described in [4] where we applied
are marked with bold. data fusion techniques (specifically, combination of data) to build
Table 2: Performance of classifiers based on various sets of features (standard deviation given in brackets; S – structural fea-
tures, T – textural features; ⋆ indicates performance that is statistically worse than performance for all features according
to two-tailed T-test)
Classifier Feature set Overall [%] Non-surgical [%] Surgical [%]
7NN S 92.5 (6.7) 89.0 (13.0) 94.0 (7.0)
T 73.4 (8.5)⋆ 57.0 (18.0)⋆ 83.0 (9.0) ⋆
S+T 94.7 (5.1) 91.0 (11.0) 98.0 (4.0)
NB S 89.2 (6.2) 84.0 (15.0) 92.0 (10.0)
T 76.1 (8.3)⋆ 73.0 (21.0) 78.0 (11.0)⋆
S+T 92.6 (5.8) 91.0 (12.0) 94.0 (6.0)
C45 S 91.4 (6.4) 92.0 (14.0) 91.0 (8.0)
T 77.0 (8.3)⋆ 85.0 (18.0) 73.0 (10)⋆
S+T 94.0 (6.7) 89.0 (11.0) 97.0 (5.0)
RIP S 92.5 (6.8) 87.0 (15.0) 95.0 (7)
T 77.0 (8.7)⋆ 77.0 (18.0) 77.0 (11.0)⋆
S+T 94.5 (5.6) 88.0 (14.0) 98.0 (4.0)
RF S 91.5 (6.9) 89.0 (12.0) 93.0 (7.0)⋆
T 77.3 (9.5)⋆ 71.0 (19.0)⋆ 81.0 (10.0)⋆
S+T 96.1 (6.5) 92.0 (11.0) 99.0 (4.0)
SVM S 90.6 (6.5) 80.0 (17.0) 97.0 (5.0)
T 80.5 (7.2)⋆ 69.0 (16) 81.0 (8.0)⋆
S+T 91.0 (6.5) 80.0 (17.0) 97.0 (5.0)
MLP S 91.5 (6.5) 88.0 (14.0) 94.0 (8.0)
T 78.4 (8.1)⋆ 72.0 (19.0)⋆ 82.0 (11.0)⋆
S+T 94.8 (5.1) 94.0 (9.0) 96.0 (6.0)
classifiers based on image and clinical data. In the additional
Figure 3: Decision rules created for the RIP classifier (the experiment we used an expanded set of image features containing
default rule for the surgical class is excluded) all structural and textural features introduced in this study. We
(MeanPeakValue <= 61.5) and (StDevPeakValue >= 35.8) observed their beneficial impact on the performance of classifiers.
and (StDevPeakValue <= 51.4) => Treatment = non-surg However, unlike previously the effect of combining image and
(61.0/0.0)
clinical data was negligible and we hypothesize that for our data
(Energy >= 0.21) and (StDevPeakValue >= 36.2)
set the set of image features are so strong predictor of the type of
and (StDevPeakValue <= 57.5) => Treatment = non-surg
(11.0/0.0) treatment that additional clinical features become redundant. We
are going to further investigate it as part of our ongoing study.
Figure 4: A decision tree created for C45 5 CONCLUSIONS
In this paper we presented the results of our study where we have
considered structural and textural features extracted from X-ray
images. We have used these features to build decision models
aimed at predicting a proper treatment (surgical vs. non-surgical)
of a patient with bone fracture and evaluated classification per-
formance of these models. Specifically, we checked the following
classifiers – k-nearest neighbor (with k = 7), naive Bayes, a
decision tree, decision rules, a random forest, a support vector
machine (with a radial basis function) and a multilayer percep-
tron. For each of these classifiers we observed an improvement
in the overall classification accuracy when using both structural
Figure 5: Attribute importance based on average impurity and textural features, and the largest increase occurred for the
decrease in RF random forest and naive Bayes classifiers. At the same time,
using textural features alone deteriorated the performance in
comparison to structural features. Hence we can conclude that
the structural features (mean and standard deviation of peak val-
ues obtained using Hough transform) have very good predictive
abilities and that they may additionally benefit from combining
them with the textural features (contrast, energy, homogeneity
and correlation).
As future work we will compare the performance of classifiers
constructed from extracted features with a convolution network
network. We also plan to use more data (e.g., more than one
image per patient) and to automate the process of fracture seg-
mentation. We are planning to consider other ensembles in the
context of data combining both image and clinical features as
this should give ensembles greater flexibility in selecting features
for component classifiers. Finally, we would like to implement
our approach in form of an educational tool that is deployed on
the Wielkopolska Center of Telemedicine platform and used by
physicians and medical students to practice their decision mak-
ing skills. This should also give us an ability to collect new data
and experience from users’ responses and to use this feedback to
improve embedded classifiers.
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