Download Figure 11. Edge Detection Using Canny Method

Detection and Classification of Breast Cancer
Nandi Nwe Win, Nang Aye Aye Htwe

Abstract — Breast cancer is the second most lethal cancer for
women in the world today. X-ray mammography is the most
widely used method for early detection of breast cancer. To
detect breast cancer region, Canny edge detection is used. To
separate this region from all other background, thresholding
method is used. This paper presents an implementation of
detection and classification system for cancerous tissues.
Malignant and benign abnormalities are selected from the
segmented images. And then texture based features are
extracted using Gray Level Difference Method (GLDM). For
the purpose of pattern classification between malignant and
benign samples, the optimum subset of texture features are
modeled by using Artificial Neural Network (ANN).Detection
and Classification of cancerous tissues is implemented with
MATLAB programming language.
Index Terms — Artificial Neural Network, Canny Operation
Digital mammograms, Feature Extraction, Gray Level
Difference (GLDM), Thresholding
I. INTRODUCTION
Breast cancer is one of the major causes for the increase in
mortality among middle-aged women, especially in
developed countries [1]. Breast cancer continues to be a
public health problem in the world specifically in western and
developed countries.The commonly used diagnostic
technique including biopsy mammography, thermography
and ultrasound image. Among these techniques
mammography is the best approach for early detection. In
early stage visual clues are subtle and varied in appearance,
because diagnosis is difficult. The abnormalities are hiding by
breast tissue structure. Breast cancer detection and
classification of mammogram images is the standard clinical
practice for the diagnosis of breast cancer. Mammography
associated with clinical breast examination is the most
efficient method for early detection of breast cancer.
However, it is very difficult to interpret X-ray mammograms
because of the small differences in the image densities of
various breast tissues, which is particularly true for dense
breasts. The interpretation of mammograms by radiologists is
performed by a visual examination of films for the presence
of abnormalities that indicate cancerous changes.
Microcalcifications, one of the early indicators of breast
cancer, are tiny granule-like deposits of calcium. The
presence of clustered microcalcifications in X-ray
Manuscript received Oct 15, 2011.
Nandi Nwe Win, Department of Information Technology, Mandalay
Technological University, (e-mail: [email protected]).
Mandalay, Myanmar, 09-256269894
Nang Aye Aye Htwe, Department of Information Technology, Mandalay
Technological University, Mandalay, Myanmar, 09-5661208 (e-mail:
[email protected]).
mammograms is considered an important indicator for the
detection of breast cancer, especially for individual
microcalcifications with diameters up to about 0.7 mm and
with an average diameter of 0.3 mm [2]. Mammography is the
only effective screening method for detection of breast cancer
in early stage. The early detection of cancer can play a
significant role in its treatment, making possible improvement
in the quality of patient’s life and increase in survival rates. If
diagnosed early, breast cancer is one of the most treatable
forms of cancer. Recently, Digital Mammograms applied for
the diagnostics of breast cancer.
I. RELATED WORKS
In the literature, various numbers of techniques are
described to detect and classify the presence of breast cancer
in digital mammograms. A lot of research has been done on
the textural analysis on mammographic images.
IndraKantaMaitra et al, [3] used GLDM features to
identification of abnormal masses and their study included
mammograms from the MIAS database.
Wu et al. [4] applied neural networks directly to the
images or the preprocessed images to recognize patterns that
might include microcalcifications in digitized mammograms.
They showed that using neural networks, clusters of
microcalcifications were more accurately distinguished in the
frequency domain than they were in the spatial domain.
Gray Level Difference Matrix (GLDM) method, introduced
by Weszka et al [5], has been widely used for analyzing
medical images. An algorithm comprising of numerous
phases to attain automatic detection of clusters was developed
by Cairns et al. [6].
Papadopoulossa et al. [7] presented a hybrid intelligent
system for the identification of microcalcification clusters in
digital mammograms, which can be summarised in
three-steps: edge detection, segmentation, feature extraction
and classification. This paper investigates the accuracy of a
detection methodology that uses Haralick Texture Features as
an input to ANN (Artificial Neural Networks) to classify the
images into benign or malignant[8]. Weidong Xu et al.
proposed a new algorithm based on ANN for detecting
masses automatically [9].
III. BACKGROUND THEORY
In this paper, there are four main parts: image acquisition,
edge detection, image segmentation, feature extraction and
classification.
A. Image Acquisition
Digital mammograms are used as the standard inputs into
the proposed framework. Mammography dataset obtained
from the Mammographic Image Analysis Society (MIAS)
database. MIAS mammography images are digitized at 200
micron pixel edge, with a size of 1024 ×1024 pixels. Each
pixel in the grayscale mammogram image represents the pixel
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International Journal of Science, Engineering and Technology Research (IJSETR)
Volume 1, Issue 1, July 2012
intensity in the range of [0, 255] (8-bit). Breast images in
MIAS database as shown in Figure 1.
extraction method for our implementation. An example of
gray level difference method is as shown in Figure 4(a) and
(b).
Figure 1. Breast images in MIAS database
B. Canny Edge Detection
The Canny edge detection is known as the optimal edge
detector. Canny edge detection aims at enhancing the many
edge detectors already published at that time. It is important
that edges occurring in images should not be missed and that
there be no responses to non-edges. Canny method is a better
method to find edges by isolating noise from the image
without disturbing the feature of edges in the image. The
experimental result of tested breast image by using Canny
method as shown in Figure 2.
Figure 4. (a) Original image (b) GLDM for Original image
(distance=1, direction=0).
The Grey-Level Difference Method is constructed based
on the statistics of the second order joint conditional
probability density function p (i | d). Where i is the grey level
(i.e. intensity) difference between two pixels. And then the
feature vectors can be derived the following the feature as
shown in Table 1.
TABLE I
DESCRIPTION OF TEXTURE FEATURES
Feature
Figure 2. Edge Detection Using Canny Method
C. Image Segmentation
The goal of Image Segmentation is to find regions that
represent objects or meaningful parts of objects.
Segmentation divides an image into its constituent regions or
objects. Thresholding has been used for segmentation as it is
most suitable for the present application in order to obtain an
image with ‘1’ representing the breast tumor
and ‘0’
representing the background. The segmented breast as shown
in Figure 3.
1
Contrast
2
Mean
3
Formula
Entropy
4
Inverse Difference
Moment
5
Angular Second
Moment
6
Area
Figure 3. Image Segmentation Using Thresholding
D. Texture Features Extraction Using Gray Level
Difference Method (GLDM)
Texture Feature extraction is a very important process in
the area of classification. Texture features have been widely
used in mammogram classification. The texture features are
ability to distinguish between abnormal and normal cases.
Gray Level Difference Method (GLDM) is a good feature
A complete set of 360 features are used for the
classification of breast image. Resulting feature vectors are
shown in Figure 5. Finally, these sets of features are used to
classify the breast images.
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input layer. The 20 hidden layer and the output layer produce
either 1 (Benign) or 0 (Malignant).
IV. SYSTEM DESIGN AND IMPLEMENTATION RESULT
A. Design of the Proposed System
In this system, Canny Method, Thresholding Technique,
Gray Level Difference Method and Artificial Neural Network
are applied to implement Breast Cancer Detection and
Classification System. In image acquisition step, we have
used the images from MIAS database. The total 80
mammograms have been used for training and testing.
These images are already processed. After applying
GLDM feature extractor following value are Contrast,
Angular Second moment, Entropy, Mean, Inverse Difference
Moment and Area. ANN Classifier is applied to these features
which classify the input image as malignant or benign. Overall
block diagram of the system is shown in Figure 7.
Input:
Image Acquisition
Edge Detection
Image Segmentation
Texture Feature
Extraction
Classification
Artificial Neural
Network
Digital
Mammogram
Result : Malignant
or Benign
Figure 5. Extracted Features
Figure 7. Overall Block Diagram of the System
E. Classification
Neural network is the best tool in pattern classification
application and composed of three layers as shown in Figure
6.
Input Layer
Hidden Layer
B. Implementation Results of the Proposed System
Detection and Classification of Cancerous Tissues System
is proposed in this system. The user can see implementation
window as shown in Figure 8 and Main Menu of the Proposed
System as shown in Figure 9.
Output Layer
Contrast
Mean
Entropy
.
.
output
.
Angular
second
moment
Inverse
difference
moment
Classification
Benign
Malignant
Area
Figure 6. Architecture of Artificial Neural Networks
The classification process is divided into the training
phase and the testing phase. The classifier is trained and tested
on mammogram image. The classification accuracy depends
on training. In the training phase known data are given. In the
testing phase, unknown data are given and the classification is
performed using the classifier after training. The accuracy of
the classification depends on the efficiency of the training.
Neural network are trained by experience, when fed an
unknown input into neural network, it can generalize from
past experience and produce a result. 6 features fed to neural
Figure 8. Cover Window of the Proposed System
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International Journal of Science, Engineering and Technology Research (IJSETR)
Volume 1, Issue 1, July 2012
After detecting the breast image, it is needed to segment
the image by using Thresholding Technique as shown in
Figure 12.
Figure 9. Main Menu of the Proposed System
The first stage of the system is to select breast images.
This can click load menu item from main menu of the
proposed system. And then required breast photo is choose
step by step as shown in Figure 10.
Figure 12. Image Segmentation Using Thresholding
Then feature points are extracted from the segmented
image using gray level difference method (GLDM), as shown
in Figure 13.
Figure 10. Load of Breast Image
In the next step, the breast image is Edge Detected using
Canny Method, as shown in Figure 11.
Figure 13. Extracted Features Points
After extracting the features, the user runs the final result
Figure 14 are results of breast classification with Malignant
and Benign.
Figure 14. Classification result of the program
Figure 11. Edge Detection Using Canny Method
To evaluate performance in this system, there are known
image from a train data set and an unknown image from a test
data set. The system’s accuracy of breast classification is
described in Table 2.
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TABLE II
THE ACCURACY RATE OF BREAST CLASSIFICATION
Images
set
Cancer
Non-ca
ncer
Tot No
Correct
Prediction
Accuracy
rate
Training
set
30
30
60
60
100%
Testing
set
50
50
100
100
100%
[9]
gray-level cooccurrence matrix approach,” J. Geophys. Res., vol.93,
pp. 12, 663–12681, Oct. 1988.
Xu W, Li L, Xu P. A new ANN-based detection algorithm of the
masses in digital mammograms. IEEE International Conference on
Integration Technology. 26-30: 2007.
V. CONCLUSIONS
Breast cancer classification is a vital stage for the
performance of the canny method of breast cancer detection.
GLDM feature vector is calculated for each image cell and is
used for better computation performance. It reduces the false
positive rate by reducing the unnecessary biopsy and health
care cost as well. ANN shows very good performance in
medical diagnostic systems. Computational time is around 36
seconds for each breast classification. It was evaluated on 60
images containing malignant and benign masses with
different size and shape. Using the ANN classifier, breast
cancer diagnosis with a training accuracy of 100% and testing
accuracy of 98% is achieved.
ACKNOWLEDGMENT
First of all, the author is grateful to her parents who
specially offered strong moral and physical support, care and
kindness. The author is highly grateful to Dr. Myint Thein, the
Pro.Rector of the Mandalay Technological University for his
permission for completion of this paper. The author is deeply
thankful to Dr. Aung Myint Aye, Dr. Nang Aye Aye Htwe,
Mandalay Technological University, for their overall
supporting during the writing of this paper.
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