International Journal of Engineering Trends and Technology (IJETT) – Volume 7 Number 2- Jan 2014
Segmentation and Classification of Lung Tumour using Chest CT
Image for Treatment Planning
Mythily.A #1, Veena.M.U *2
#
P.G. Student & Department of Biomedical Engineering, Udaya School of Engineering, Tamilnadu, India
*Assistant Professor & Department of Biomedical Engineering, Udaya School of Engineering, Tamilnadu, India
Abstract— With a fast development of computed tomography
technology, CT images has become one of the most efficient
examination method to detect lung diseases in clinical. By using
lung CT image, automatic segmentation is done in order to assist
the surgeons to remove the portion of lung for the treatment of
certain illness such as lung cancer, and tumours. In the proposed
method tumour is diagnosed and classified. The main blocks
involved in the proposed system are image pre-processing,
segmentation of lung region, feature extraction from the
segmented region, classification of lung cancer as benign or
malignant. Initially pre-processing is used to remove the noise
present in CT image using weiner filter, and then segmentation is
performed using adaptive threshold algorithm. Tumour is
extracted from lung by using Seeded region growing algorithm.
Features extracted from the lung tumours using gray level cooccurrence matrix (GLCM). For classification, Support Vector
Machine (SVM) classifier is used. The main aim of the method is
to develop a CAD system for finding the lung tumor using the
lung CT images and classify the tumor as Benign or Malignant.
The results indicate a potential for developing an algorithm to
segment lung tumour and to detect the benign and malignant
tumour for treatment planning.
Keywords— Computed Tomography (CT), Tumour, Wiener
Filter, Adaptive threshold, Seeded region growing algorithm,
gray level co-occurrence (GLCM), Support vector machine
(SVM).
I.
INTRODUCTION
Cancer, tumor and nodules that are to be examined are
located within the lungs parenchyma, in an area which is
usually no more than half of the area of the computed
tomography (CT) image slice: this means that a lot of
processing time can be saved if the segmentation algorithms
only run on this inner part of the lungs area. Moreover, the
number of false detection of lesions found in a segmented
image is dramatically lower than that found in the same image
without segmentation, because no signals at all will be found
outside of the lungs. Lung segmentation is a common preprocessing step of lung CAD systems and in general of lung
image analysis systems. CT datasets can contain over 1000
images; therefore manually segmenting the lungs is tedious
and prone to inter observer variations. That is why an
automatic segmentation algorithm for lung extraction from CT
patient image is implemented and tested [21]. The complexity
for finding the lung nodules in radiographs are as follows:
 Nodule sizes will vary widely: Commonly a nodule
diameter can take any value between a few mille meters up to
several centi meters.
 Nodules are usually with a great variation in density.
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 As nodules can appear anywhere in the lung field,
they can be hidden by ribs, the mediastinum and structures
beneath the diaphragm, resulting in a huge variation of
contrast to the background.
II.
LUNG DISEASE
Lung diseases [11] are some of the most common medical
conditions worldwide. Tens of millions of people suffer from
lung disease worldwide. Smoking, infections, and genetics are
responsible for most lung diseases [19]. Some of the common
lung disease can be divided in these groups:
 Lung Diseases Affecting the Airways;
 Lung Diseases Affecting the Air Sacs (Alveoli);
 Lung Diseases Affecting the Interstitium;
 Lung Diseases Affecting Blood Vessels;
 Lung Diseases Affecting the Pleura;
 Lung Diseases Affecting the Chest Wall.
Most occurred ones are: asthma, acute bronchitis, cystic
fibrosis, emphysema, pneumonia, tuberculosis, emphysema,
pulmonary edema, lung cancer, sarcoidosis, and different kind
of pulmonary edemas.
A. Lung Imaging
Imaging plays a vital role in the diagnosis of lung cancer,
with the most common modalities including chest
radiography, CT, PET, magnetic resonance imaging (MRI),
and radionuclide bone scanning [10], but in this work, we
primarily used CT images for analysis.
X-Ray imaging will show most lung tumors, but CT is
used because it is more sensitive in finding tumor size and the
presence of lymph node metastases. However, with CT
imaging, it is not always easy to distinguish the limits between
tumor and normal tissue, especially when the dense pathology
is present. Recent advances in Computed Tomography (CT)
technology have enabled its use in diagnosing and quantifying
different diseases [4]. In particular, the expanding volume of
thoracic CT studies along with the increase of image data,
bring in focus the need for CAD algorithms to assist the
radiologists [2]. Several lung diseases are diagnosed by
investigating the patterns of lung tissue in pulmonary CT
images, therefore segmentation and analysis is one of the
important parts of CAD systems [12, 15].
B. CT Scan
CT or computer axial tomography, uses special X-ray
tube to obtain image data from different angles around the
body, and then uses computer processing of the data to show a
cross section of body tissues and organs. Some of the basic
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ideas underlying CT are reconstruction from projections that
means that the patient data is getting measured at many
positions and angles. CT modalities can show various types of
tissues, lung, soft tissue and bones, and using specialized
equipment and expertise to create and interpret CT scans of
the body, radiologists can more easily diagnose tumor, cancer
or other lesion, and to measure its size, precise location, and
the extent of the tumour’s involvement with other nearby
tissue. The images received from a CT scanner can reveal
some soft tissue and other structures that cannot even be seen
in conventional X-rays.
C. Computer-Aided Diagnosis
Computer aided diagnosis of lung CT image has been a
remarkable and revolutionary step, in the early and premature
detection of lung abnormalities. The CAD systems include
systems for automatic detection of lung nodules and 3D
reconstruction of lung systems, which assist the radiologists in
their final decisions. Advanced image processing algorithms
are applied on the images to clarify and enhance the image
and then to separate the region of interest from the whole
image. The separately obtained region is then analyzed for
nodule detection, tumor or cancer to diagnose the disease [7].
Efficient lung segmentation technique helps to raise the
accuracy and higher decision confidence value of any lung
abnormality identification system.
Computer-aided diagnosis may serve as a second reader
by analyzing nodules, lesions or tumor and providing a
malignancy estimate using computer vision and machine
learning techniques. CAD may address some of the issues in
diagnosis characterization, such as the increasing demand on
radiologist's time caused by the increasing data volume,
radiologist fatigue or distraction, and differences in
radiologists' experience. Computers are playing an
increasingly large role in radiology. In conventional
radiography, X-ray images were recorded on screen-film
systems, while today's radiologists view digital radiographs on
display monitors. Computers have been vital in the
development of medical imaging technology – without
computerized reconstruction, CT and MRI imaging would not
be possible. The next role for computers may be in the
interpretation of images. A CAD system comprises
segmentation, feature extraction, feature selection, and
classification components. We aim to develop an effective
CAD system that will assist radiologists in setting the right
diagnosis.
CAD systems for medical images typically involve the
steps of segmentation the image, extraction of various region
of interest and classification of that area. Various algorithms
from different authors can be found for medical image
segmentation such as thresholding [13], region growing [8, 9].
These methods may be effective for specific types of disease;
segmentation of lungs is always a challenging problem due to
changes in pathology in the parenchym area, or in shape and
anatomic connection to neighboring pulmonary structures,
such as blood vessels or pleura.
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Different window width and level settings can also effect
the image reconstruction [5]. These factors contribute to
inaccuracies in the measured volume [3], resulting in
inconsistency and uncertainty in detecting volume change in
serial CT scans. Even for the identical imaging conditions, CT
images will contain difference in image data, sometimes from
the detector itself, noise received from the CT modality or the
detectors, noise from the patient itself, or from the different
procedures of the technician that is performing the scan. One
example is that the starting scan position or sometimes the
standard table height of the CT scanner is not always
reproducible, as well as the slice locations relative to the
anatomical structures are therefore not identical in repeated
scans. In clinical CT exams, variability’s such as patient
motion and the change in pitch size or topogram
characteristics over time will further degrade the
reproducibility between exams.
To overcome these problems, in this paper proposed a
Computer Aided Diagnosing (CAD) [1] system for detection
of lung nodules [14]. The lung cancer detection system is
shown in Figure 1:
IMAGE
ACQUISITION
PREPROCESSING
SEGMENTATION
FEATURE
EXTRACTION
CLASSIFICATION
USING SVM
TUMOUR REGION
SEGMENTATION
ANALYSIS
Fig. 1 Block Diagram of proposed system
This paper initially apply the different image processing
techniques such as image pre-processing for removing noise
in the image, tumour segmentation using seeded region
growing algorithm, feature extraction using GLCM and SVM
for classification is used.
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III.
IMAGE PRE-PROCESSING
Pre-processing is done to remove the noise present in the
CT image. In order to remove the Gaussian noise, Wiener
filter is used. Since Wiener filter gives an estimate of the
original uncorrupted image with minimum mean square error,
and hence Wiener filter is used for removing the noise present
in the lung CT image [18].
The CT Images normally contains artefacts, noise which
will not be suitable for further processing and hence it has to
be pre-processed to reduce the noise using Wiener filter.
Wiener filter is used because of following advantages.
 It takes very short time to find the optimal solution as
the apriori knowledge about the image is taken into
consideration.
 It controls the output error as it is optimal.
 It is straightforward to design and it is faster.
 It is a least mean square filter.
The disadvantages of Wiener filter is that the results are
too blurred and it is spatially invariant, but these are not
observed in the experimentation result and hence Wiener filter
works better when compared with respect to other. The main
constraint in the use of Wiener filtering is that signal and
noise should be Gaussian processes for optimality. The noise
in the image can be modelled as Gaussian. Gaussian is the
natural way of modelling noise. Therefore Wiener filter is
used for removal of noise in CT image. Wiener filer gives an
estimate of the original uncorrupted image with minimum
mean square error and the estimate is the non linear function
of the corrupted image. Wiener filters of size 3*3 are used to
remove the e noise present in the CT image, two of the error
metrics are used to evaluate the filter with mean square error
(MSE) and the peak signal to noise ratio (PSNR). The mean
square error is the cumulative squared error between the
compressed and the original image, whereas peak signal to
noise ratio is a measure of the peak error.
IV.
SEGMENTATION OF LUNG REGION
Image segmentation is a necessary task for image
understanding and analysis. Image segmentation plays an
important role in a variety of applications such as robot vision,
object recognition, and medical imaging [12]. In the field of
medical diagnosis an extensive diversity of imaging
techniques is presently available, such as radiography,
computed tomography (CT) and magnetic resonance imaging
(MRI). In recent times, Computed Tomography (CT) is the
most effectively used for diagnostic imaging examination for
chest diseases such as lung cancer, tuberculosis, pneumonia
and pulmonary emphysema. The volume and the size of the
medical images are progressively increasing day by day.
Therefore it becomes necessary to use computers in
facilitating the processing and analyzing of those medical
images.
This is a technique which often provides better results is to
only use edge points when creating the grey level histogram.
First the seed point in the CT image has to be chosen. From
the point intensity value of the image is found. Then the
intensity value between the neighbouring pixels and current
pixel is compared. If the neighbour pixels values are related to
the seed value, it will segment from the original image. These
similarity pixels will be segment from the CT image. This
process is continued until reach the last pixel.
Fig. 2: Segmented Lung
1) Adaptive Threshold: First a gray-level T between
those two dominant levels is selected, which will serve as a
threshold to distinguish the two classes (objects and
background). Where the value marked as T is a natural choice
for a threshold. Using this threshold, a new binary image can
then be produced, in which objects are painted completely
black, and the remaining pixels are white.
Denote the original image f(x, y), then the threshold
product is achieved by scanning the original image, pixel by
pixel, and testing each pixel against the selected threshold: if
f(x, y) > T, then the pixel is classified as being a background
pixel, otherwise the pixel is classified as an object pixel. This
can be summarized in the following definition, where b(x, y)
denotes the threshold binary image.
In the general case, a threshold is produced for each
pixel in the original image; this threshold is then used to test
the pixel against, and produce the desired result (in our case, a
binary image).
According to this, the general definition of a threshold
can be written in the following manner:
T= T [x, y, p(x, y), f(x, y)]
where f(x, y) is the gray level of point (x, y) in the original
image and p(x, y) is some local property of this point (we shall
explain this shortly). When T depends only on the gray-level
at that point, then it degenerates into a simple global threshold
(like the ones described in the previous section). Special
A. Lung Segmentation
In this, the left and right lung is segmented from the CT attention needs to be given to the factor p(x, y). This was
image by using the adaptive threshold segmentation algorithm. described as a property of the point.
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Actually, this is one of the more important components in
the calculation of the threshold for a certain point. In order to
take into consideration the influence of noise or illumination,
the calculation of this property is usually based on an
environment of the point at hand. An example of a property
may be the average gray-level in a predefined environment,
the center of which is the point at hand.
B. Fissure Detection
Pulmonary fissure is detected by the use of eigen values.
A nonzero vector x is an eigenvector (or characteristic vector)
of a square matrix A if there exists a scalar λ such that Ax =
λx. Then λ is an eigenvalue or characteristic value of A.
Matrix acts on a vector by changing both its magnitude and its
direction. However, a matrix may act on certain vectors by
changing only their magnitude, and leaving their direction
unchanged or possibly reversing it. These vectors are the
eigenvectors of the matrix. A matrix acts on an eigenvector by
multiplying its magnitude by a factor, which is positive if its
direction is unchanged and negative if its direction is
reversed. This factor is the eigenvalue associated with that
eigenvector.
C. Lobe Segmentation
Watershed transformation is a common technique for
image segmentation [23]. However, its use for automatic
medical image segmentation has been limited particularly due
to over segmentation and sensitivity to noise. Employing prior
shape knowledge has demonstrated robust improvements to
medical image segmentation algorithms and proposed a novel
method for enhancing watershed segmentation by utilizing
prior shape and appearance knowledge. In watershed internal
markers is used to obtain watershed lines of the gradient of the
image to be segmented. Use the obtained watershed lines as
external markers. Each region defined by the external markers
contains a single internal marker and part of the background.
Instead of working on an image itself, this technique is often
applied on its gradient image.
V.
FEATURE EXTRACTION
Image feature extraction is very important stage of
computer based system. Feature extraction provides certain
parameters, on the basis of which computer system takes
decision. After the segmentation is performed on lung region,
the features can be obtained from it and the diagnosis rule can
be designed to detect nodules in the lung.
The entire feature which are calculated from the image,
convey some information regarding lung nodule. This
information is very helpful in detecting lung nodule as
malignant or non-malignant. Thus the features extracted from
the CT image can be used as diagnostic indicators [6].
The features that are used in this study are Texture features
using Co- occurrence matrix representation.
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A. GLCM
Gray level Co-occurrence matrix (GLCM) based texture
feature extraction introduced by Haralick et.al and Mari partio
et.al has been considered as the powerful technique and still
now has been used in many applications of remote sensing for
texture analysis. GLCM method comes under the statistical
approach of texture analysis which describes texture as a set
of statistical by R.M.Haralick measures based on the spatial
distribution of gray levels within the band of the remotely
sensed imagery. GLCM matrix is computed from a relative
displacement vector (d,) which is formed based on the relative
frequencies of gray level pairs of pixels separated by a
distance d in direction [16]. Haralick suggests 14 texture
statistical measures based on GLCM matrix and the most
popularly used texture measures are as follows,
Where,
pd is the probability matrix obtained through GLCM;
μ is the mean of pd and 6y the standard of pd(x) and pd(y)
respectively.
GLCM measures calculated and depicted for each pixel.
1) Algorithm for GLCM: The steps for extracting
texture features of image using GLCM can be given as below
[16].
 Separate the R, G, B planes of image.
 Repeat steps 3-6 for each plane.
 Compute four GLCM matrices (directions for
δ=00, δ=450, δ=900, δ= 1350) as given by eq.
 For each GLCM matrix compute the statistical
features Energy (Angular second moment), Entropy (ENT),
Correlation (COR), Contrast (CON).
 Compute the feature vector using the means and
variances of all the parameters.
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Fig. 3 GLCM
The figure 3 shows how gray co-matrix calculates several
values in the GLCM of the 4-by-5 image I. Element (1,1) in
the GLCM contains the value 1because there is only one
instance in the image where two, horizontally adjacent pixels
have the values 1 and 1. Element (1, 2) in the GLCM contains
the value 2 because there are two instances in the image where
two, horizontally adjacent pixels have the values 1 and 2.
Gray co-matrix continues this processing to fill in all the
values in the GLCM.
VI.
classes; the vectors closest to the boundaries are called
support vectors and the distance between the support vectors
and hyper plane is called margin [20].
Support vector machines are supervised learning models
with associated learning algorithms that analyze data and
recognize patterns, used for classification. The basic SVM
takes a set of input data and for each given input predicts
which of two classes forms the input, making it a nonprobabilistic binary linear classifier. From given set of
training examples, each marked as belonging to one of two
categories, an SVM training algorithm builds a model that
assigns new examples into one category or the other. In the
proposed method we are using linear classifier. Best hyper
plane is the one that represents the largest separation or
margin between the two classes. So we choose the hyperplane
so that the distance from it to the nearest data point on each
side is maximized [22]. If such a hyper plane exists, it is
known as the maximum margin hyperplane and the linear
classifier it defines is known as a maximum classifier, which
is shown in fig.5.
CLASSIFICATION USING SVM
A. Support Vector Machines
SVM introduced by Cortes is generally used for
classification purpose. SVM is a machine learning technique
which is used as a classification tool.It uses kernel function,
which acts upon the input data; final summation with an
activation function gives the final classification result.
Fig. 5 Maximum margin classifier
Fig. 4 Architecture of SVM
The architecture of SVM is shown in Fig.4, in which the
suffix “n” represents number of vectors. Ns denote the number
of support vectors. A binary classification is used here, in
which a hyper plane classifies the given data into two different
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SVMs are universal approximators which depend on the
statistical and optimizing theory. The SVM is particularly
striking the biological analysis due to its capability to handle
noise, large dataset and large input spaces [17].
The fundamental idea of SVM can be described as
follows:
 Initially, the inputs are formulated as feature vectors.
 Then, by using the kernel function, these feature
vectors are mapped into a feature space.
 Finally, a division is computed in the feature space to
separate the classes of training vectors.
A global hyper plane is sought by the SVM in order to
separate both the classes of examples in training set and avoid
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over fitting. This phenomenon of SVM is more superior in
comparison to other machine learning techniques which are
based on artificial intelligence. The mapping of the inputoutput functions from a set of labeled training data set is
generated by the supervised learning method called SVM. In a
high dimensional feature space, SVM uses a hypothesis space
of linear functions which are trained with a learning technique
from optimization theory that employs a learning bias derived
from statistical learning theory. In Support Vector machines,
the classifier is created using a hyper-linear separating plane.
It provides the ideal solution for problems which are not
linearly separated in the input space. The original input space
is non-linearly transformed into a high dimensional feature
space, where an optimal separating hyper plane is found and
the problem is solved. A maximal margin classifier with
respect to the training data is obtained when the separating
planes are optimal.
For binary classification SVM determines an Optimal
Separating Hyperplane (OSH) which produces a maximum
margin between two categories of data. To create an OSH,
SVM maps data into a higher dimensional feature space and
carries out this nonlinear mapping with the help of a kernel
function. Then, SVM builds a linear OSH between two classes
of data in the higher feature space. Data vectors that are closer
to the OSH in the higher feature space are known as Support
Vectors (SVs) and include all data necessary for classification.
VII.
TUMOUR REGION SEGMENTATION
A. Basic concept of seed points
The first step in region growing is to select a set of seed
points. Seed point selection is based on some user criterion
(for example, pixels in a certain gray-level range, pixels
evenly spaced on a grid, etc.). The initial region begins as the
exact location of these seeds [24].
The regions are then grown from these seed points to
adjacent points depending on a region membership criterion.
The criterion could be, for example, pixel intensity, gray level
texture, or color.
Since the regions are grown on the basis of the criterion,
the image information itself is important. For example, if the
criterion were a pixel intensity threshold value, knowledge of
the histogram of the image would be of use, as one could use
it to determine a suitable threshold value for the region
membership criterion.
B. Some important issues
Then we can conclude several important issues about
region growing are:
 The suitable selection of seed points is important.
 More information of the image is better.
 The value, “minimum area threshold”.
 The value, “Similarity threshold value”.
C. Advantages and Disadvantages of Region Growing
We briefly conclude the advantages and disadvantages of
region growing algorithm.
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1) Advantages:
 Region growing methods can correctly separate the
regions that have the same properties we define.
 Region growing methods can provide the original
images which have clear edges the good segmentation results.
 The concept is simple. We only need a small
numbers of seed point to represent the property we want, then
grow the region.
 We can determine the seed point and the criteria we
want to make.
 We can choose the multiple criteria at the same time.
 It performs well with respect to noise.
2) Disadvantages:
 The computation is consuming, no matter the time or
power.
 Noise or variation of intensity may result in holes or
over segmentation.
 This method may not distinguish the shading of the
real images.
We can conquer the noise problem easily by using some
mask to filter the holes or outlier. Therefore, the problem of
noise actually does not exist.
D. Seeded region growing algorithm
Seeded region growing method constructs regions by
starting from some user provided voxels called seeds. The
region grows from this seed point by comparing the values of
neighboring voxels from this seed point. Since the aim of this
part of the segmentation process is the removal of the dark
areas adjoining the lung nodule, a seed voxel must be selected
from the dark region. The approach selected here finds the
minimum voxel from the image on the boundary. This is a
dark voxel (grey level 0) in most cases.
Fig. 6 Defining tumor boundary through region growing
Once the dark voxel coordinates are selected, region
growing starts in the form of marking all the voxels which lie
in the 8-connected neighbourhood of the seed voxel. The
process is repeated for each voxel until all the black voxels are
classified in the image which adjoin the areas of the lung
nodules. This deletes the dark regions that are connected to
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the border of the image. To distinguish between the white
regions already present in the lung nodules and the white
regions generated because of the background air volume, the
white regions of the lung nodules are tagged before region
growing starts.
Seeded region growing is one of the best methods to
segment tumor regions as the borders of regions found by
region growing are perfectly thin and connected. Defined
tumor boundary through region growing is shown in figure 6.
VIII.
CONCLUSION
Proposed CAD system helps physician to evaluate
whether the tumor is benign or malignant. The computer
tomography image is used in this paper. The image is first preprocessed then lung region is segmented and features
extraction is performed by GLCM. Finally with the obtained
texture features classification is performed by using SVM
classifier to detect the occurrence of cancer nodules.
ACKNOWLEDGMENT
Thank God for the wisdom and perseverance that he has
been bestowed upon us throughout our life.
We would like to express our greatest gratitude to the
people who have helped & supported us.
A special thank goes to our friend who helped us in
completing the work and exchanged her interesting ideas.
Last but not least, we wish to thank our parents for their
undivided support and interest who inspired us and
encouraged to go our own way, without whom we would be
unable to complete our work.
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