PERFORMANCE EVALUATION OF CURVILINEAR STRUCTURE REMOVAL ...

1-02 Performance Evaluation Of Curvilinear Structure Removal Methods In Mammogram Image Analysis

PERFORMANCE EVALUATION OF CURVILINEAR STRUCTURE
REMOVAL METHODS IN MAMMOGRAM IMAGE ANALYSIS

Setiawan Hadi
Informatics Engineering FMIPA University of Padjadjaran
Jalan Raya Bandung Sumedang KM 21 Jatinangor 45363

email: [email protected]

ABSTRACT tissue are blocked by other tissues with curvilinear structure.
This can occur when the surrounding cancerous regions are
Image preprocessing algorithms for improving the also other tissues such as the breast tissue breast milk and
performance of breast cancer detection algorithm have blood vessels. At mammography activities, the curvilinear
been evaluated and their performance are reported in this structure of tissues gives the same reaction with suspected
paper. Those algorithms are implemented successfully in cancer tissue with the almost identical image intensity to the
different sequence combination, those are (i) intensity diseased tissue. Due this condition, we need some efforts to
adjustment before removal of curvilinear structure and (ii) remove the curvilinear structures in mammograms so that the
intensity adjustment after removal of curvilinear structure. area of the suspected cancer can be more easily detected.
Intensity adjustment task applied thresholding technique and
removal of curvilinear structure task applied convolution 2 PROBLEM DESCRIPTION
process. Experiment has been conducted using 20 images
taken from the mini-MIAS database of mammograms. It 2.1 Mammography Activity
can be concluded that the result will assist the appropriate
selection of preprocessing method for accurate breast cancer Mammography is the process of using low power X-rays
information extraction from mammograms. energy to examine the human breast. This activity can be
considered as a diagnostic and a screening tool. The goal
Keywords: Curvilinear, mammogram, image, thresholding, of mammography is the early detection of breast cancer,
breast, cancer typically through detection of characteristic masses and/or
microcalcifications.
1 INTRODUCTION
Like all X-rays, mammograms use doses of ionizing radia-
Breast cancer is the second deadly disease that involve tion to create images. Radiologists then analyze the images
women all over the world after cervical cancer [1]. It is for any abnormal findings (see illustration on Figure 1).
reported there are more than 1,500,000 new cases of breast Other devices can be used in mammography activity such as
cancer globally including 226,870 new cases in USA [2]. ultrasound, ductography, positron emission mammography,
This cancer usually affects adult women between the ages and MRI. Ultrasound is typically used for further evaluation
of 40 to 49 years. The cause of the disease is unknown, but of masses found on mammography or palpable masses not
one of them is suspected related to genetic factors. seen on mammograms. Ductograms are still used in some
institutions for evaluation of bloody nipple discharge when
Early detection of tumors in the breast area can be perfor- the mammogram is non-diagnostic. MRI can be useful for
med by mammography breast screening through radiology further evaluation of questionable findings as well as for
and X-ray pictures that are known as mammograms [3]. screening pre-surgical evaluation in patients with known
This activity is safe due to low-dose radiation. The goal of breast cancer to detect any additional lesions that might
mammography is to detect lumps in the breast, even for a change the surgical approach, for instance from breast-
very small to feel ourself. conserving lumpectomy to mastectomy.

Mammogram images resulted from mammography activity Digital mammography is a specialized form of mammo-
not only present view of suspected cancer, but also displays graphy that uses digital receptors and computers instead of
other tissues such as vascular tissue and milk gland organs X-ray film to help examine breast tissue for breast cancer.
contained in the breast. Therefore, to be able to detect The electrical signals can be read on computer screens,
suspected cancerous area accurately, some efforts should be permitting more manipulation of images to theoretically
done to detect the presence of tissue other than cancer. allow radiologists to more clearly view the results.

The location of a cancer on mammograms has two cha- 2.2 Mammogram Image
racteristics. In most of the mammogram, the cancer tissue
appears in a collection with certain mass so that it can Mammography activities produce a mammogram image.
be detected easily. In contrast, in many cases, the cancer It is used for detection of woman breast cancer who has felt
the presence of symptoms such as lumps and pain, or who do

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The Proceedings of The 7th ICTS, Bali, May 15th-16th, 2013 (ISSN: 9772338185001)

Figure 1. Normal (left) and cancerous (right) mammography image [4] 3) the suspected area is not visible due to the growth of
cancer cells cancer does not affect or alter the normal
tissue around it

Areas of suspected cancerous tissue that are hidden behind
normal breast could occur if the surrounding areas are part
of normal breast such as breast tissue and breast milk blood
vessels. Because most of the healthy tissue is in the form
of stripes with a curve like the curve with relative constant
change of size of the thickness, then the term ”curvilinear
structure” of the normal breast tissue is defined. Figure 3
shows breast with curvilinear structure.

not feel any symptoms at all. Mammograms not only show
the suspected cancerous tissue, but also show other tissues
such as blood vessels and tissue milk gland organ located on
the breast. Figure 2 shows abnormal mammograms.

Figure 3. Breast with curvilinear structure and cancer tissue [6]

Figure 2. Breast calcification (top), Fibrocystic breast tissue (left below), At the time of mammography activities, tissues forming
Breast tumor (right below). [5]. curvilinear structure give the same reaction with tissue
suspect cancer. Consequently, the image intensity is almost
2.3 Curvilinear Structure on Mammogram similar to diseased tissue and the process of differentiating
objects based on intensity gray level becomes difficult to
There are two characteristics of the location of a cancer on perform.
mammograms. The first characteristic occurs in the condition
in which areas of suspected cancer is clearly seen in mam- 2.4 Computer-based Breast Cancer Detection Methods
mogram. In this case, a suspicious cancer object is grouped
somewhere with a certain mass that can be detected easily. Computer-based breast cancer detection methods have
Furthermore, these objects have more contrast intensity than been proposed and published in the literature. In [7], breast
other objects do. So the distinction can be seen easily by cancer is detected and classified using neural network. The
intensity differences of the gray level. classification of micro cancer object of breast tumor has
been performed based on feed forward back propagation
The second characteristic of cancerous condition occurs neural network. Twenty six hundred sets of cell nuclei cha-
where the tissue is difficult to find. There are three things racteristics obtained by applying image analysis techniques
that can cause the condition, those are: to microscopic slides of Hematoxylin and Eosin stained
samples of breast biopsy have been used in that work. Other
1) suspected area is located in the location which is method that has been widely used is the watershed seg-
difficult to detect by mammography mentation method [1], [8], [9], combined with other image
processing methods. A mathematical set based technique
2) suspected cancerous area is hidden behind normal called morphological operations is also used in detecting
breast breast cancer [10]. This method is applied to a mammogram
that produces a high contrast image, which can be further
enhanced for segmentation steps which leads to an easy
identification of cancerous portion.

3 PROPOSED METHOD

Preprocessing step is a basic image processing task that
is usually performed to prepare mammogram images before
recognition process. It consists of various intensity adjust-
ment and segmentation algorithms applied to the images.

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1-02 Performance Evaluation Of Curvilinear Structure Removal Methods In Mammogram Image Analysis

In this paper two preprocessing methods are evaluated to using convolution technique as follows:
support a breast cancer detection algorithm. The first method
is to apply intensity adjustment using thresholding technique R = w1z1 + w2z2 + . . . + wmnzmn
before the process of curvilinear structure removal. The
second method is to apply thresholding after the process of mn
curvilinear structure removal.
= wizi
Before implementing the methods, mammogram images
are converted to gray-level image and then inverted. The i=1
resulted image is similar with an X-ray image (see Figure 4).
where w is mask coefficient, z is image gray-level
Figure 4. Xray-like mammogram image value, correspondence with w. Sum of all coefficient
in the mask w is m × n. If necessary, average filtering
3.1 Thresholding Before Curvilinear Structure Removal operation can be applied to remove curvilinear lines
around the object. The curvilinear structure removal
The algorithm of this method can be described as follows: is performed by applying smooth filtering operation.
This operation will reduce image interference (noise)
1) Mammogram label removal: in this task label of as well as to give the effect of blurring the image,
mammogram obtained from mammographic activity is removal of small details in the image and disguise line
removed manually. Basic image processing cropping or curve of an object with other objects in the back-
task can be used for finishing this task. ground. Mathematically, the operation of smoothing
is calculation of average pixel within a neighborhood
2) Histogram-based gray-level value selection for thre- area. By replacing the value of each pixel with the
sholding: in this task selection of grey-level value for average value of all pixels in a neighborhood region,
thresholding operation is performed by evaluating pixel the pixel differences between the focus pixel and its
distribution in histogram of mammogram image using adjacent pixels can be minimized. The matrix filter
formula h(rk) = nk where rk is kth grey-level value, mask to calculate the average pixel neighborhood can
and nk is sum of pixel with rk value. Since the area of in any size 3 × 3, 5 × 5 or 7 × 7 pixels. Figure 5
suspected cancerous tissue is brighter than the others illustrates this result.
which means it has greater gray-level value, then the
value of gray level that meets characteristics of can- Figure 5. Result of curvilinear structure removal
cerous tissue should be selected from the right which
has a small frequency histogram (y axis value). The 5) Marking cancer region in mammogram image: This
selected value then is used in thresholding operation is final steps to clarify areas of cancerous tissue on
(thresholding value T ). the mammogram image by combining processed image
with original images and by drawing white color pixels
3) Thresholding operation: This operation is performed on areas that suspected as cancer tissues.
based on this code:
3.2 Thresholding After Curvilinear Structure Removal
f (x, y) = 0 if f (x, y) ≤ T The algorithm of this method can be described as follows:
M axIntensity if f (x, y > T 1) Mammogram label removal: in this task label of
mammogram obtained from mammographic activity is
where T is thresholding value obtained from previous removed manually. Basic image processing cropping
step. In this step, all pixel value f (x, y) which is task can be used for finishing this task.
smaller than threshold value will be replaced by 0, 2) Curvilinear structure detection: To perform curvili-
and all pixel value greater than threshold value will near structure detection, filtering operation is applied in
be replaced by M axIntensity (maximum intensity) order to obtain a binary image which can be considered
value, in this case is 255. as candidate curvilinear objects from mammogram
4) Removal of curvilinear structure: This task consists image. The operation is performed through a convo-
of two steps. First is detection of curvilinear structure lution with the first derivative Sobel kernel. Sobel
and the second is the removal of curvilinear structure. suggests the use of kernel size region 11 × 11 to the
The detection of curvilinear structure is performed neighborhood at 0◦, 15◦, 30◦, 45◦, 60◦, 75◦, 90◦, 115◦,
130◦, 145◦, 160◦, and 175◦. The selection of kernel
size must me performed carefully so the size is not

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The Proceedings of The 7th ICTS, Bali, May 15th-16th, 2013 (ISSN: 9772338185001)

greater that the size of mammogram image. The use resulting Figure 8.

of accurate kernel size will affect the discovery of the

mammary gland and blood vessels. Figure 6 shows the

result of convolution of selected mammogram image
with Sobel kernel size of 5 × 5 and direction of 0◦,
45◦ and 90◦. The thresholding values are 124.7, 124.5

and 129.0, respectively.

Figure 8. Final result of cancer detection

Figure 6. Combined image as result of Sobel filtering operation 4 EXPERIMENTAL RESULT

When compared to the original mammogram image Experiment has been conducted using 20 mammogram
(see Figure 3), it appears that each white pixel in images selected from Mammographic Image Analysis So-
original mammogram image has brighter color than the ciety (MIAS) database [11]. It consists of 10 health mam-
pixels in surrounding area of combined mammogram mograms and 10 mammograms with cancer. Each image was
image. Those points form smooth curves that show processed using both methods described before. Performance
the existence of mammary glands and blood vessels in evaluation indicators that have been monitored are:
the breast. Lines forming curvilinear structure will be
further eliminated through removal stage of curvilinear • The value of threshold on thresholding segmentation
structure. stage on both methods. The selected threshold values
3) Curvilinear structure removal: Elimination of curvi- are between 210 to 238.
linear structures is done by replacing the gray-level
value with the average value neighborhood points • Sobel kernel size on the implementation of the first
through convolution with a kernel smoothing process. derivative
After experimenting of applying smoothing filter on
original mammogram image by using kernel size 3×3, • The threshold value on the implementation of the first
7 × 7, 11 × 11, 20 × 20, 30 × 30, and 50 × 50, it is derivative, and
known that the best kernel size of smoothing is 30×30.
When using 11×11 kernel size, the mamary glands and • The size of the kernel image smoothing.
blood vessels are intact. Figure 7 shows the result of
implementing 30 × 30 kernel on original mammogram The experimental result is displayed on the Table 1. As we
image. saw in the table, 14 mammogram images have been success-
fully detected by both methods. 6 mammogram images were
Figure 7. Original image processed by 30 × 30 kernel size only successfully detected using method A, 2 mammogram
images, mdb013.pgm and mdb021.pgm, were unsuccessfully
4) Thresolding Segmentation: To improve clarity of the detected using both methods.
areas of cancer, thesholding segmentation performed
oby using image sharpening technique. This operation The oversegmentation condition and inaccurate selection
will highlight areas on mammograms with the value of thresholding value have been suspected as the cause of
of gray level higher than the surrounding area. Thre- these failure of detection. The oversegmentation problem
sholding value are selected between 210 and 238. The occurred when the tissue that suspected as cancer region
output image is then combined with original image does not have curvilinear structure but it has a homogeneous
areas with similar gray-level values with healthy surrounding
tissues of the breast. This area can not be classified as a
curvilinear structure, but as a region. Figure 9 shows the
unsuccessfully detected of mammogram images.

Other experimental result on images mdb007.pgm,
mdb008.pgm and mdb011.pgm gave not only accurate result
but also other curvilinear structure. However this condition
may not affect the whole result. Figure 10 displayed this
condition.

5 EVALUATION

Based on our experiment, evaluation for future research
can be described as follows:

1) Selected thresholding value is between 210 and 235.
In fact, this interval can be dynamically changed due
to the image characteristics. Other methods of thre-
sholding such as Otsu thresholding could be used for
improve the detection

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1-02 Performance Evaluation Of Curvilinear Structure Removal Methods In Mammogram Image Analysis

Table 1. Experimental Result removal, is superior than Method B. It is concluded that
the thresholding task is very essential to be performed
MIAS Ref. No. Class of Detection result before other image processing tasks
abnormality
mdb001.pgm Method A Method B ACKNOWLEDGMENT
mdb002.pgm Cancer
mdb003.pgm Cancer Success Success This work is supported by DP2M DIKTI through Compe-
mdb004.pgm Normal tition Grant Research 2013. Appreciation is given to the UN-
mdb005.pgm Normal Success Success PAD Research Center, the Faculty of MIPA, the Mathematics
mdb006.pgm Cancer Departments, and the VISiLab for managing, supervising and
mdb007.pgm Normal Success Fail facilitating the research.
mdb008.pgm Normal
mdb009.pgm Normal Success Fail REFERENCE
mdb010.pgm Normal
mdb011.pgm Cancer Success Success [1] R. Ramani, N. Suthanthira Vanitha and S. Valarmathy, A Comparative
mdb012.pgm Normal Study of Algorithms for Breast Cancer Detection in Mammogram,
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mdb014.pgm Cancer
mdb015.pgm Normal Success Success [2] R. Siegel and A. Jemal, Cancer Facts & Figures 2012, American
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mdb018.pgm Cancer [3] L. Fassa, Imaging and cancer: A review, Molecular Oncology, 2, 2008,
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Figure 9. Unsuccessfully detected of mammogram images

Figure 10. Successful detected of mammogram images and other structures

2) Experiment showed that small Sobel kernel size 3×3 or
5 × 5 is enough for detection thin curvilinear structure,
however, the size should be expanded to detect thicker
curvilinear structure. One should be considered that the
larger the kernel size, the more resources are required.
In addition, the result might be less accurate.

3) Based on the experiment, the direction of Sobel kernel
that fit for this detection are 0◦, 45◦ and 90◦. These
directions are relatively easy to performed due to its
special nature of angle.

4) Method A, thresholding before curvilinear structure

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