Ijciet 10 02_012

http://www.iaeme.com/IJCIET/index.asp 96 editor@iaeme.com
International Journal of Civil Engineering and Technology (IJCIET)
Volume 10, Issue 02, February 2019, pp. 96-105, Article ID: IJCIET_10_02_012
Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=10&IType=02
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication Scopus Indexed
EXTRACT THE BREAST CANCER IN
MAMMOGRAM IMAGES
Farah Q. Al-Khalidi, Bassam Alkindy, Thekra Abbas
Department of Computer, College of Science, Mustansiriyah university, Baghdad, Iraq
ABSTRACT
Image processing techniques play a significant role in many areas in life, especially
in medical images, where they play a prominent role in diagnosing many diseases such
as detection of the brain tumor, breast cancer, kidney cancer, and the fractions.
Breast cancer is a common disease, regardless of the type of this disease, whether
it is benign or malignant, it is very dangerous and early detection may reduce the risk
of the disease spreading in the body leading to death. This work presents an approach
to detect breast cancer based on image processing algorithms, including image
preprocessing, enhancement, segmentation, Morphological operations, and feature
extraction to detect and extract the breast cancer region.
Keywords: Brest cancer, Mammogram segmentation, Morphological operations.
Cite this Article: Farah Q. Al-Khalidi, Bassam Alkindy, Thekra Abbas, Extract the
Breast Cancer in Mammogram Images, International Journal of Civil Engineering and
Technology, 10(02), 2019, pp. 96–105
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=02
1. INTRODUCTION
Breast cancer for both genders begins in the breast cells and expands to other parts of the body.
In today's world, breast cancer is highly preponderant. It is the second most common type of
cancer after lung cancer. Therefore, early diagnosis of cancer is crucial.
Masses are the first sign of malignant cancer which determined by the spaces that can be
realized by lesions and can be indicated by their constructional figures and marginal
characteristics.
The second most common sign of breast cancer is architectonic disfiguration which is
accepted with progression in improving domain, a new term has been presented by the
information technology (IT) as Medical Image Processing (MIP), which has an investigating
distinct feature in different fields. Detecting of a mass as cancer in an infected breast becomes
easy by using image-processing techniques.
The mammogram image is the first test used for checking and diagnosing the breast cancer,
therefore (analysis and processing images) proposed as keys to improving cancer diagnosis.

Cabrera et al. in [1] suggested an efficient analysis of mammogram images to reveal a
tumor in the early stages depending on texture segmentation.
Kral and Lenc in [2] proposed an algorithm for detecting cancer using mammogram images
of the breast. The proposed algorithm effectively uses ((LBP) Local Binary Patterns) based
features for classification in addition to thresholding. The algorithm evaluated on a subset of
images selected from (MIAS) and (DDSM) databases that have achieved 84% of accuracy.
Lenardo et al. in [3], proposed masses detection algorithm on digital mammograms images.
They suggested using K-means algorithm to segment the image with a co-occurrence matrix
for describing each segment texture.
The idea of medical diagnosis of breast cancer is not new but a suitable approach for early
detection of cancer is still a challenging one. With progression in computer sciences, IT
contribution has introduced a new dimension termed as Medical Image Processing (MIP). By
using image-processing techniques, it has become easier to discover cancer masses in an
infected body.
The computer-aided detection method described as a first preprocessing step, [4] removes
any unwanted noise and enhances the features of the image. The second step is the
segmentation, which achieved by dividing the image into small parts. Then, the classification
technique applied to extract the useful features.
Vasantha and Bharathi [5] tried to improve the filtering method to implement noise-
removing process. This technique achieved by applying low-pass and high-pass filters. The
first filter process removes noise while distorts the edges, smooth the image but chokes the
image information. Therefore, the second filter is useful to enhance image information.
Nagi J. [6] suggested to use adaptive mean filter method which developed by processing a
region inside a rectangle shape. Applying the aforementioned method makes the image
smoother by filling the information of the image neighborhood without blurring the edges and
maintaining image details.
Shan et al. [7] suggested a promising method called mean filter, which takes the identical
intensities from the neighborhood pixels and replaces each unclear pixel with that identical
intensities. This method produces a smooth image, improves the image quality and eliminates
the Gaussian noise.
Thangavel et al. [8] proposed a histogram equalization method based on pre-processing the
mammogram images and improving the gray-scale quality. This method used to enhance the
contrast illumination of that image and obtain better views. Another approach proposed by
Maitra et al. [9]. They suggested a seeded region growing method. The seed of the tumor
region grows into large population providing connected regions. The method can be robust and
restriction free, based on order of pixel processing.
2. GENERAL PRESENTATION
The proposed method for the detection of breast cancer uses a two-stage pipeline wherein each
stage an action is applied. These stages are summarized as follow: the selection of mammogram
breast image and the processing operations will then applied using two suggested methods. The
latter technical methods detect and extract the cancer region in the mammogram breast image.
Figure 1 illustrates the block diagram of the proposed method.

Extract the Breast Cancer in Mammogram Images
Figure 1: Block diagram of the proposed method.
3. METHODOLOGY
3.1. Manual Segmentation Technique
In medical images, manual segmentation technique is commonly used. The specialist depends
on the visual observation of the desired area. Although this method is rapid and easy to
implement, it suffers from difficulties with applying it to complex shapes [10]. Figure 2 shows
the implementation of manual extraction the cancer region.
Figure 2: Manual Extraction of cancer region.
3.2. Detect and Extract the Cancer Region Automatically
The major steps to detect and extract the cancer region from the mammogram breast image are:
• Enhance the breast image using Average filter
• Histogram equalization
• Binarization the thresholding mammogram image.
• Apply Morphological Operation
• Isolation the cancer region
• Extract the features of this region.

3.2.1. Enhancement the Image
The Mammogram image like any other images suffered from the noise. The average filter is
used to remove the unwanted noise. Figure 3 illustrates the applying of the average filter to
the mammogram breast Image.
Figure 3: Applying average filter to the input of mammogram image.
3.2.2. Histogram Equalization
The essential step in detecting the cancer region in the mammogram is the histogram
equalization, where the relative frequency of the pixels in a given image is shown. There is no
uniform changeable of the image, due to external conditions, to a uniform shape. Figure 4
shows the implementation of the histogram equalization to the filtered image.
Figure 4: Histogram equalization for the filtered image.
3.2.3. Binarization the Threshold Image
To determine the boundary of the breast from the rest of the image, the filtered mammogram
image needs to convert to a binary image. This technique achieved by Binarization the
threshold image [11] as illustrated in Figure 5.

Figure.5: Binarization the thresholded image
3.2.4. Morphological Operation
During this step, a comparison takes place between regions and neighborhood while indexing
each region with a binary image. A morphological technique named (Structural pixels) is used
to evaluate a small shape or template. The structuring pixels are placed in different possible
locations in the image and then matched to the pixels of the identical neighborhood. The
process then starts by examining the pixel to classify if it is "fit" within the neighborhood, or
else, it "hits" or intersects the neighborhood.
The tumor region is represented by the region with the greatest intensity which has gone
through subsequent detection by labeling the regions with numbers.
3.2.5. Isolation the Cancer Region
The high-density in the image determine the isolation of the cancer region from the remaining
mammogram image. Figure 6 shows the detection and extraction of the cancer region from the
mammogram breast image.
Figure 6: Detection and Extraction of the cancer Region in mammogram breast image.
4. RESULTS AND DISCUSSIONS
The proposed application is created using MATLAB software [12, 13] as shown in Figure 7.
The GUI allows users to load the mammogram breast image and then isolated the cancer region
from the remaining image; the statistical features with histogram then extracted from this
region. This procedure will open the way in the future to use these features to distinguish the
cancer region either benign or malignancy.

Figure 7: GUI for Proposed System
To measure the accuracy and the sufficient of this method, 400 mammogram breast images
are considered for testing to localize the cancer region; the suggested method is successful in
isolating the cancer region for 380 (95%) of images in a correct manner.
However, the remaining (5%) of unsuccessful images is noticed either by the wrong way
of taking the image or because of the high blurring ratio of mammogram breast image.
Therefore, the obtaining results compared with the manual segmentation process; the overall
error of detection process calculated based on Equation 4:
(1)
Where EC is the overall error, MC is the missed cancer regions, and FC is the false-positive
of cancer parts detected.
The percentage of error for this approach is calculated using Equation 2:
%
100
(2)
Where T: Total number of tested images.
Figure 8 illustrates the accuracy yet the advantages gained by this approach in detecting the
tumor region in the human breast Mammogram images.

Figure 8: Detection of Cancer Region in the mammogram image.
3.2.6. Features Extraction for the Region of Interest (ROI)
After detecting and extract the ROI the features are calculated which includes:
Mean: One of the statistical features that studied for the Region of Interest (Cancer region)
is the Mean. This feature calculated using Equation 3.

,
1
,
, ∈!
(3)
Where g(x, y) is the filtered image, f(x, y) is the original image before being filtered, S is
the set of coordinating pixels in the neighborhood of pixel(x, y) including the pixel(x, y) itself,
and N is the total number of pixels in the neighborhood [12].
Variance: The second statistical features that extracted from the region of interest (Cancer
region) are the Variance. This feature is calculating using Equation 4.
"#
∑ % − ' #
(4)
The variance (σ2
), is defined as the sum of the squared distances of each term in the
distribution from the mean (μ), divided by the number of terms in the distribution (N).
Standard Deviation: An important statistical feature derived from the Region of Interest
(Cancer region) is the Standard Deviation. This feature is calculating using Equation 5.
() *+
1
, ×
- , − #
./0
12
3/0
12
(5)
The Histogram: The other feature extracted from the ROI is the Histogram. Then calculate
the average of the four highest values for the frequencies in the histogram of the ROI. The
histogram for the cancer region(ROI) of the mammogram breast image is shown in Figure 9.
Figure 9: Histogram for the cancer region.
Figure 10 explained the role of the suggested method to isolate the ROI (Cancer region) in
the mammogram breast image with the features extracted for this region.

Figure 10: The Cancer Region with its Features.
5. CONCLUSION AND FUTURE WORK
This approach introduced a new method to discover the breast cancer region from the
mammogram images based on image processing techniques, which include enhancement,
morphological operations, segmentation, feature extraction and isolation of the cancer region.
Isolate the cancer region is based on the segment the image into several parts. The greatest
intensity part represented the breast cancer region.
Further work is to expand this technique in order to provide more accurate cancer
classification by considering more breast cancer types.
ACKNOWLEDGMENT
The authors would like to thank Mustansiriyah University (www.uomustansiriyah.edu.iq),
Baghdad-Iraq for its support in the present work.
REFERENCES
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