Identification of masses in digital mammogram using gray level co-occurrence matrices
1 Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia
2 Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia
3 Department of Electrical, Electronics and
Systems, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Selangor, Malaysia
Digital mammogram has become the most effective technique
for early breast cancer detection modality. Digital mammogram takes an
electronic image of the breast and stores it directly in a computer. The aim of
this study is to develop an automated system for assisting the analysis of
digital mammograms. Computer image processing techniques will be applied to
enhance images and this is followed by segmentation of the region of interest
(ROI). Subsequently, the textural features will be extracted from the ROI. The
texture features will be used to classify the ROIs as either masses or
non-masses. In this study normal breast images and breast image with masses
used as the standard input to the proposed system are taken from Mammographic
Image Analysis Society (MIAS) digital mammogram database. In MIAS database,
masses are grouped into either spiculated, circumscribed or ill-defined.
Additional information includes location of masses centres and radius of
masses. The extraction of the textural features of ROIs is done by using gray
level co-occurrence matrices (GLCM) which is constructed at four different
directions for each ROI. The results show that the GLCM at 0�, 45�, 90� and
135� with a block size of 8X8 give significant texture information to identify
between masses and non-masses tissues. Analysis of GLCM properties i.e.
contrast, energy and homogeneity resulted in receiver operating characteristics
(ROC) curve area of �for Otsu�s method,
0.82 for thresholding method and for K-mean clustering.
ROC curve area of 0.8-0.9 is rated as good results. The authors� proposed
method contains no complicated algorithm. The detection is based on a decision
tree with five criterions to be analysed. This simplicity leads to less
computational time. Thus, this approach is suitable for automated real-time
breast cancer diagnosis system. � 2009 Biomedical Imaging and Intervention
Journal. All rights reserved.
Keywords: Breast cancer, Mammogram, Masses, GLCM
Breast cancer has become a significant health problem in
the world. Early detection is the primary solution for improving breast cancer
prognosis. Screening can be done through digital mammogram, ultrasound,
magnetic resonance imaging (MRI) or breast biopsy. Ultrasound produces a good
contrast image but it does not contain enough detailed information which can be
found in digital mammogram. Due to this reason, ultrasound is not approved by
the U.S Food and Drug Administration (FDA) as a screening tool for breast
cancer . Although MRI is more sensitive than digital mammogram, its results
can also lead to false positive diagnosis which then leads to unnecessary additional
tests, biopsies and increased patient anxiety. In addition, the American Cancer
Society recommends MRI for women with approximately 20-25% or greater lifetime
risk of breast cancer, including women with strong family history of breast or
ovarian cancer . The benefit of digital mammogram in helping to detect
breast cancer early, obviously outweigh the other methods discussed previously.
This support the fact that many studies have found that digital mammogram is
better at detecting early stage breast cancer [3-14, 16-21].
Although digital mammogram has been proven to be an
effective method for detecting breast cancer, interpretation of such mammograms
requires skill and experience by a trained radiologist. It is noted that about
10-30% of breast lesions are missed during routine screening . Independent
double reading by two radiologists has been shown to improve the sensitivity,
but it also increased the cost of the screening process . Thus,
computer-aided detection (CAD) can act as a second reader where the final
decision will be made by the radiologist.
The combination of CAD scheme and an expert�s knowledge
will greatly improve the detection accuracy. CAD system has been developed in
mammogram for detection of either mass or micro-calcification (MCC). Detection
of masses using digital mammogram is more challenging because masses are
usually indistinguishable from the surrounding breast tissues. Normally, masses
are hidden or are found in the dense area and are similar to other normal tissues
in the breast. Some of the typical shapes of masses are spiculated,
circumscribed and ill-defined. Irregular shapes of masses are usually found to
Several studies were published on the automatic detection
of masses exploring varieties of computational techniques [4, 5-12, 18-21].
Some researchers used multi-view images in CAD system [5, 6]. In particular,
Bovis and Singh proposed bilateral subtraction of both mammograms (right and
left) in order to find asymmetries in either mammogram . This method,
however, leads to a low sensitivity and high false positive rate due to
instrinsic breast asymmetry. In their work, classification is based on texture
features extracted from the region of interest (ROI). Exploiting information in
two views of the same breast taken at some oblique angles, mediolateral oblique
(MLO) and craniocaudal (CC), also improved the performance of CAD .
Most CAD only uses a single mammogram. He Wan et al. carried
out pre-processing procedure using exponential transformation and then
extracted six features from the suspicious region . The binary decision tree
is adopted as classifier due to its conceptual simplicity and computational
efficiency. Mohamed and Kadah proposed a three-step system, namely the ROI
extraction, features extraction and classification. A set of 88 features are
extracted and they found that 78 of those features are capable of
discriminating between normal and abnormal breast tissues in digital mammogram
with True Positive (TP) rate of 83.3% . Dominguez and Nandi also built a three-step CAD
system, but the steps involved were pre-processing, segmentation and
elimination of false positive findings. In their pre-processing step, wavelet
decomposition and reconstruction, morphological operations and local scaling
are used for enhancement of digital mammogram. Then, the segmentation process
is performed via conversion to binary images at multiple threshold levels and
18 features are extracted and used for detection with TP rate of 80% .
Automatic detection of masses using artificial neural network (ANN) has also
been considered by a few researchers [11, 12]. Weidong Xu et al. proposed
a new algorithm based on ANN for detecting masses automatically . In fatty
tissues, iterative thresholding was applied to locate masses and for masses in
dense tissues, black hole registration based on discrete wavelet transform
(DWT) was used instead. Finally, the segmented suspicious masses were filtrated
using 10 selected features via multilayer perceptrons (MLP) classifier, which
gave a TP rate of 93.6%. Guodong Zhang et al. used automatic segmentation, 10
selected features for detection of a suspicious area and achieved sensitivity
of 83.3% .
Based on the above literature, a better detection rate can
be achieved with more features included in the system. However, having more
features increase the complexity and time used to analyse the digital
mammogram. In this paper, the authors would like to propose a simple CAD system
to automatically detect areas that have a high probability of masses in digital
mammogram. The detection process is illustrated in Figure 1. This system uses
only four features and gives relatively good TP rate as compared to the
literature discussed above.
Digital mammogram database
The mammogram images used in this experiment are taken
from the mini mammography database of MIAS.
In this database, the original MIAS
database are digitized at 50 micron pixel edge and has been reduced to 200 micron
pixel edge and clipped or padded so that every image is 1024 X 1024 pixels. All
images are held as 8-bit gray level scale images with 256 different gray levels
(0-255) and physically in portable gray map (pgm) format. This study solely
concerns the detection of masses in mammograms and, therefore, a total of 100 mammograms
comprising ill-defined, spiculated, circumscribed and normal case were
considered. Ground truth of location and size of masses is available inside the
Mammograms are medical images that are difficult to
interpret, thus a pre-processing phase is needed in order to improve the image
quality and make the segmentation results more accurate. The first step
involves the removal of artefact and unwanted parts in the background of the
mammogram. Then, an enhancement process is applied to the digital mammogram.
The contrast limited adaptive histogram equalization
(CLAHE) method seeks to reduce the noise produced in homogeneous areas and was
originally developed for medical imaging . This method has been used for
enhancement to remove the noise in the pre-processing of digital mammogram
. CLAHE operates on small regions in the image called tiles rather than the
entire image. Each tile�s contrast is enhanced, so that the histogram of the
output region approximately matches the uniform distribution or Rayleigh
distribution or exponential distribution. Distribution is the desired histogram
shape for the image tiles. The neighbouring tiles are then combined using
bilinear interpolation to eliminate artificially induced boundaries. The
contrast, especially in homogeneous areas, can be limited to avoid amplifying
any noise that might be present in the image. The block diagram of
pre-processing is shown in Figure 2. The experimental results of
enhancement on digital mammogram using CLAHE have been reported in the authors�
previous work .
In analyzing mammogram image, it is important to
distinguish the suspicious region from its surroundings. The methods used to separate
the region of interest from the background are usually referred as the
segmentation process. The segmentation block diagram is shown in Figure 3. The
first method used in this study is the local threshold technique. This
technique has been proven to provide an easy and convenient way to perform the
segmentation on digital mammogram . The segmentation is determined by a
single value known as the intensity threshold value. Then, each pixel in the
image is compared with the threshold value. Pixel intensity values higher than
the threshold will result in a white spot in the output image.� Therefore,
experimental work has been conducted and also reported in the authors� previous
work. The results show the detection of ROI that contain masses is 96% .
For comparison, another two methods of segmentation have
been investigated. K-mean clustering is a region clustering method that does
not need prior information or start point and is based on an iterative process
. This method only requires a stop function, which is the number of
clusters, , in the segmented
image. The higher the �value, the clearer the
segmentation but the processing time will increase. An experiment was conducted
and the optimum value of , as used in this
study, was found to be 4. K-mean segmentation output gives a TP rate of 96%.
The second method for comparison is the Otsu�s method,
which has shown a more satisfactory performance in the medical image segmentation.
It has been found to perform well compared to other thresholding methods in
segmenting the masses in digital mammogram .� In this study, the Otsu�s method is able to segment the ROI with a TP rate of 90%.
The output of a segmentation process is a binary image. In
order to retrieve the texture information, the segmented image is masked with a
16-bit quantization image. Instead of using the original image, a quantized
image is used. In the quantized image, the amount of represented intensities is
visible to humans. By reducing quantization level to 16 bits, the area with
masses can be identified on the mammogram as shown in Figure 5. The masked
image is then used as input for the features extraction process.�
Features extraction and selection
Texture features have been proven to be useful in
differentiating masses and normal breast tissues [5, 16, 19]. Texture features
are able to isolate normal and abnormal lesion with masses and
microcalcifications, yielding values of 0.957 and 0.859, respectively, from the
area under the curve (ROC) . In the authors� experimental work, the texture
features are extracted using gray level co-occurrence matrices (GLCM). The
matrices are constructed at a distance of �and for direction of q� given as �and . A
single direction might not give enough and reliable texture information. For
this reason, four directions are used to extract the texture information for
each masses and non-masses tiles area. �
The texture descriptor derived from GLCM are contrast,
energy, homogeneity and correlation of gray level values. Table 1 provides the
equations for the four features. The contrast measures the amount of local
variations present in an image, while energy is the sum of squared elements in
GLCM. Energy may also be referred as uniformity or the angular second moment.
The homogeneity descriptor refers to the closeness of the distribution of
elements in GLCM to the GLCM diagonal. Lastly, correlation will show how
correlated a pixel is to its neighbour over the whole image.
Based on the authors� database, the biggest masses area is
within a 32x32 window and the smallest is within 8x8 window. Therefore, the
authors use window sizes of 8x8, 16x16 and 32x32 for this study. Features
extraction and selection block diagram is shown in Figure 5. The processing
window or tiles is important because it will determine the ability of the
texture descriptor to differentiate between the masses and the normal breast
tissues. Note that the selection of area should be done randomly. As
illustrated in Figure 6, masses and non-masses areas are captured using 8x8,
16x16 and 32x32 windows.
Detection of masses
Detection is important in selecting the candidate regions
that highly resemble masses in terms of their intensity and statistical texture
value. The process is done based on block processing windows or tiles.
Therefore, the entire mammogram is divided into tiles area before extraction of
features is done to each tile. In this work, detection is implemented in two
phases. Phase I would be a preliminary round for detecting any suspicious area
with windows of bigger sizes. Thus, the segmented image is divided into tiles
with a size of 32x32 and a tile would be categorized as suspicious if its
average intensity is more than 200. This threshold value is chosen after
extensive investigation on pixel intensity of masses areas. The intensity
comparison is applied to each region in the segmented image and regions or
tiles that do not fall into this category are rejected.�
Those regions which are qualified in phase I will be taken
as inputs for phase II. Phase II involves a more detailed process. The 32x32
windows are divided into smaller windows with size of 8x8. Then, a tile is
considered to be suspicious if its average intensity within 8x8 tiles is more
than 210. After that, its texture criteria are evaluated. The tiles are
considered as masses if their texture criteria values are within the range of
masses texture values. The overlap criterion is used for validation of the
proposed method. The flow of detection process is illustrated in Figure 7. The
detection is considered true positive (TP) if the region of interest overlaps
with the area of groundtruth circle, otherwise the detection is a false
positive (FP). Efficiency of detection system is analysed based on four cases,
which are false positive (FP), false negative (FN), true positive (TP), and
true negative (TN). The definitions of these cases are given in Table 6.
To evaluate the performance of detection, specificity and
sensitivity of detection have been considered. Sensitivity and specificity are
terms that show the significance of a test related to the presence or absence
of the disease. Equations (2.1) and (2.2) are used to calculate these two
In particular, sensitivity indicates the number of
subjects who have the disease and are accurately identified by positive test.
Thus, it is a measure of the probability of correctly diagnosing a condition.
Specificity indicates the number of subjects who do not have the disease, and
are accurately identified by negative test. Thus, it is a measure of the
probability of correctly distinguishing when the condition is not present in a
Other statistical method known as relative operating
characteristic (ROC) curve is also used to analyse the experimental results.
ROC curve is a graphical plot of the sensitivity against� specificity for a
binary classifier system as its discrimination threshold is varied . The
ROC can also be represented equivalently by plotting the fraction of true
positive rate (TPR) against the fraction of false positive rate (FPR). A ROC
curve demonstrates the trade off between sensitivity and specificity in which
the closer the curve to the 45�
diagonal of the ROC space, the less accurate the test. At the same time, the
area under the curve is also a measure of the accuracy. ROC curve in this study
is plot using Analyse-it software. An area of 1 represents a perfect test,
while an area of 0.5 represents a worthless test.
In this work, 20 abnormal regions have been used for
groundtruth purpose and another 20 normal images were randomly selected for
texture extraction. The range of values contrast, homogeneity, energy and
correlation of masses and non-masses tissues of 8x8 tile area are shown in
Table 2, Table 3, Table 4 and Table 5, respectively.
It is found that the best features for discriminating
masses are the features of GLCM constructed at direction of 0�. For masses area, the contrast, homogeneity
and energy ranges are 0.00-0.07, 0.96-1.00 and 0.70-1.00, respectively.
Similarly for non-masses area, the contrast, homogeneity and energy ranges are
0.27-0.73, 0.63-0.80 and 0.16-0.36, respectively. It is also observed that the
values of contrast, homogeneity and energy for masses area and non-masses area
are highly discriminated. This has proven the usefulness of the three texture
descriptors in differentiating the masses and non-masses tissues. It is also
observed that, the contrast and homogeneity are two significant texture
descriptors, but energy is shown to be the most effective discriminator as
portrayed in Figure 8, Figure 9 and Figure 10a, respectively. Figure 10b showed
that the energy extracted from tiles size of 16x16 provides less accurate than
the energy of tiles sized 8x8. Note that the energy for tiles size 32x32, shown
in Figure 11, failed to differentiate between masses and non-masses area.
Correlation applied to any processing block has, however, shown no
significance, thus we conclude that correlation cannot be used to differentiate
between masses and non-masses tissues. Figure 12 portrayed the correlation
result for 32x32 tiles. This result have been reported in the authors' previous
Detection is done based on textural descriptors obtained
from features extraction process. The sensitivity and specificity of Otsu's method in detecting masses using GLCM features is 70% and 100%, respectively. The sensitivity
of local threshold is 72% and K-mean is 74% and their corresponding specificity
is 88% and 74%, respectively. The overall classification accuracy is shown in
Table 7. Three ROC curves computed from masses detection results of local
threshold, Otsu's and K-mean methods using GLCM features are shown in Figure
13. The curve of Otsu's method with GLCM features follows the closest to the
left-hand corner as compared to the others. This demonstrates that the Otsu's method is the most accurate technique in detecting masses.
As shown in Table 8, the mean area under the ROC curve
using GLCM features for Otsu�s method is 0.84. With good rating, the results
also prove that Otsu�s method with GLCM features classification gives more
accurate results than local threshold and K-mean in the CAD for masses
Discussion and Conclusion
False positive (FP) and false negative (FN) cases are
considered errors in these experiments because they will degrade the overall
performance of the detection techniques. Referring to the histogram shown in
Figure 13, Otsu�s method with classification based on GLCM features shows
the best performance as it produces less error than the other two segmentation
methods. Segmentation with Otsu�s method, with classification using GLCM
features produces only 15 errors (0 FP and 15 FN) , the local threshold�
produces 20 errors with 6 FP and 14 FN, while K-mean performs worst with
total error of 26 (13 FP and 13 FN).
In brief, Otsu�s method with GLCM features for
classification of masses obtained good results in detecting any types of masses
in digital mammogram. Without any complex algorithm, using a decision tree with
only three GLCM features to be analysed, this detection system still able to
give a good rating of area under the ROC curve with . This
approach has potential for further development because of its simplicity that
will motivate online or real-time breast cancer diagnosis in providing a second
opinion to radiologists.
The authors would like to thank Multimedia University for providing the research fund for this project and their colleagues at Multimedia University for their helpful comments.
Figure 1 The proposed method of detection for masses in digital mammogram.
Figure 10 (a) Energy value at q=0�using tile 8x8 pixels. (b) Energy value at q=0�using tile 16x16 pixels
Figure 11 Energy value at q=0�using tile 32x32 pixels.
Figure 12 Correlation value at q=0�using tile 32x32 pixels.
Figure 13 ROC curve for classification of masses using GLCM features.
Figure 14 Experimental results for classification of masses using GLCM features.
Figure 2 Image pre-processing block diagram.
Figure 3 Segmentation block diagram.
Figure 4 Features extraction and selection block diagram.
Figure 5 16-bit gray level quantization provides texture information better than original image.
Figure 6 Selection of area for features extraction process.
Figure 7 Decision tree for masses detection using GLCM features.
Figure 8 Contrast value at q=0�using tile 8x8 pixels.
Figure 9 Homogeneity value at q=0�using tile 8x8 pixels.
Table 2 Contrast values of masses and non-masses (Tile=8x8)
Table 3 Homogeneity values of masses and non-masses (tile=8x8)
Table 4 Energy values of masses and non-masses (Tile=8x8)
Table 5 Correlation values of masses and non-masses (tile=8x8)
Table 6 Definition of detection cases
Table 7 Sensitivity and specificity
Table 8 Area under the roc curve for masses classification using GLCM features
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|Received 1 April 2009; received in revised form 24 May 2009,
accepted 3 June 2009
Correspondence: Faculty of Engineering, Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor, Malaysia. E-mail: firstname.lastname@example.org (Azlindawaty Mohd. Khuzi).
Please cite as: Mohd. Khuzi A, Besar R, Wan Zaki WMD, Ahmad NN,
Identification of masses in digital mammogram using gray level co-occurrence matrices, Biomed Imaging Interv J 2009; 5(3):e17