Post on 07-Apr-2023
BICAD: Breast Image Computer Aided Diagnosis for Standard BIRADS 1 and 2
in Calcifications
Elizabeth López-Meléndez1, Luis David Lara-Rodríguez
1, Estela López-Olazagastí
2,
Bárbara Sánchez-Rinza1, Eduardo Tepichin-Rodríguez2
.
1 Facultad de Ciencias de la Computación, Benemérita Universidad Autónoma de Puebla, 14 Sur y avenida San Claudio.
Ciudad Universitaria, San Manuel, 72570 Puebla, México 2 Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro # 1, Tonantzintla, 72840, Puebla, México
elom17764@gmail.com, luisregis1984@gmail.com, estelalo@inaoep.mx, brinza@cs.buap.mx, tepichin@inaoep.mx
Abstract The Breast Imaging Reporting and Data System
(BIRADS) was developed by the American
College of Radiologists as a standard of
comparison for rating mammograms and breast
ultrasound images. It sets up a classification for
the Level of Suspicion (LOS) of the possibility of
a breast cancer. In this paper we present an
automated image analyzing system that finds
calcifications based on the standard BIRADS 1
and 2. For our goal, we studied the digital
mammography database in DICOM format
provided by the Department of Radiology of the
Hospital Universitario de Puebla. We used The
Difference of Gaussian (DOG) filter to find
edges of the forms of the different calcifications
and a back-propagation Artificial Neural
Network (ANN) for the pattern recognition of
the BIRADS 1 and 2 cases. This method allowed
us to automate the segmentation of the
calcifications with a low computational cost. We
achieved the pattern recognition with a high
level of sensitivity of 0.9629 and specificity of
0.9920.
1. Introduction
It is well known that cancer is a disease
in which abnormal cells grow in an uncontrolled
way. In this direction, Breast cancer is a
malignant growth that begins in the tissues of
the breast. Unfortunately, is the most common
cancer in women, but it can also appear in men
[1-3]. In order to obtain an early diagnosis,
different techniques of Breast imaging are
usually applied [3-4]. These imaging studies
mainly include: mammograms, ultrasounds,
MRI, PET scans, CAT scans, and elastography
[1-7].
The corresponding diagnosis report
(mammography, MRI, US) incorporates many
technical details, including one section with a
Breast Imaging Reporting and Data System
(BIRADS) score. This report indicates the
radiologist's opinion of the absence or
likelihood of breast cancer [8-12].
As mentioned before, BIRADS was
developed by the American College of
Radiologists as a standard of comparison for
rating mammograms and breast ultrasound
images. It sets up a classification for the Level
of Suspicion (LOS) in masses and calcification,
from 0 to 6. In other words, rates the possibility
of breast cancer in the studies [13-14].
As reported in the literature, BIRADS 1
and 2 for calcification, corresponds to the 90%
most incident cases for radiologists [15-16].
Taking into account this information, we focus
the study in our paper to this to cases.
Therefore, the aim of our paper is to present a
CAD system that can help the radiologist to
have a second look for a better diagnosis. The
system finds only BIRADS 1 and 2 using DOG
filter and image processing to find the regions
of interest and the feature vector. To recognize
these forms we use a back-propagation ANN.
To achieve our goal the rest of the paper
is organized as follows; section 2 describes the
image processing of the mammograms; section
3 explains the feature extraction and pattern
recognition procedure; section 4 shows the
obtained results, and finally section 5 outlines
the conclusions and future work.
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2. Image Processing of the Mammograms
We start our study by showing, in table
1.1 and 1.2, the forms that are included in the
BIRADS 1 and 2 references, only for
calcification.
Table 1.1 Forms that are part of BIRADS 1.
Name Form
Canes
Eggshell
Dystrophic
Vascular
As cited before, the mammography
database that was used in this work was
obtained from the department of Radiology
from the Hospital Universitario de Puebla. It
contains 366 cases with 4 images each one with
a spatial resolution of 50 µm, with an 8bit depth.
Table 1.2 Forms that are part of BIRADS 2.
Name Form
Sutures
Pop Corn
Round
Punctuate
Some related works are: Dengler,
Behrens and Desaga in 1993 Segmented
Microcalcifications in analogic mammograms,
they used the so called Difference of Gaussian
filter (DOG) to segment the microcalcifications
[17-19]. Bin Zheng in 2000 used DOG filter to
find clusters of microcalcifications and masses,
he had a spatial resolution of 100 µm, and the
database used was the DDSM database [20-21]. Salfity et al. and others authors [22-26] used
DOG filter for Detection and Classification of
Clustered Microcalcifications in Mammograms.
We present in Fig. 2.1, a flowchart
which explains the processing of the images
(Mammograms).
BIRADS 1 & 2
DOG Filter
Automatic Threshold
Labeling of Regions Feature Extraction
ANN Training
Record and Display results
Pattern Recognition
Figure 2.1 Flowchart of the System detection.
2.1 DOG Filter
The Difference of Gaussian is a filter
that helps to identify edges; The DOG performs
edge detection by performing a Gaussian blur
on an image at a specified sigma. The resulting
image is a blurred version of the source image.
Then, the algorithm performs another blur with
a sharper sigma that blurs the image less than
previously. The final image is then calculated by
replacing each pixel with the difference between
the two blurred images and detecting when the
values cross zero [27].
A Gaussian blur filter can be described
mathematically as:
,kee2
1y,xG
222222 2yx2yx
2
(1)
Where x is the distance from the origin
in the horizontal axis, y is the distance from the
origin in the vertical axis, and σ is the standard
deviation of the Gaussian distribution.
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Using a two-Gaussian blur steps, we can
obtain a DOG filter, which can be written as:
.ekeky,xDOG
22
2221
22 2yx
2
2yx
1
(2)
In our work, we used a DOG filter with
two different kernel sizes of 0.25mm and
0.75mm with a value of of 1.7 and 2,
respectively.
2.2 Automatic Threshold
In the proposed detection system and
after the image is passed by a DOG filter, we
obtain an automatic threshold. To do so, we first
binarize it, then we took 5% of the total pixels,
and the obtained percentage is the value of the
threshold [28]. The 5% was obtained in an
experimental way; the purpose was to eliminate
the exceeding noise caused by the DOG filter.
2.3 Preprocessing of the Labeled Regions
After the binarized image are obtained
and in order to obtain the regions of interest in
the image, we need to do some processing. First
we erode the image; the resulting image is the
close edge of the calcifications. With that close
edge, the next step is fill those edge so we can
get a complete region [28]. Then, we need to
label the different regions, with the procedure
described below.
2.4 Labeled Regions
The procedure used in this step is known
as the connected component labeling algorithm
it consists in subsets of connected components
that are uniquely labeled based on a given
heuristic. The vertices contain information
required by the comparison heuristic, while the
edges indicate connected 'neighbors'. An
algorithm traverses the region, labeling the
vertices based on the connectivity and relative
values of their neighbors. Connectivity is
determined by the medium; image graphs, for
example, can be 4-connected or 8-connected.
This connected-component labeling is
typically used in computer vision to detect
connected regions in binary digital images, and
will allow us to find the forms of the region. In
our proposed procedure, we used 8-connected
neighbors, to find and count them [27].
We take some metrics of the regions to
create a feature vector, in the next table 2.1 we
can see the metrics used in this work.
3. Feature Extraction and Pattern
Recognition
In pattern recognition and in image
processing, feature extraction is a special form
of dimensionality reduction. When the input
data is too large then the input data will be
transformed into a reduced representation set of
features (also named features vector).
Transforming the input data into the set of
features is called feature extraction.
In our case, we use the metrics given in
table 2.1. From these 15 metrics, we standardize
the values obtained of each form (calcifications)
and create a feature vector, the way we create
the feature vector was using an standard
deviation of 0.01 for each metrical.
Table 2.1 Metrics Obtained from the Regions
Metrical Formula
Area
f cNf
f
Nc
c
cfR1 1
,
Perimeter
pNp
p
pR1
Maximum
Diameter
(M.D)
2
122
12 yyxxmax
Minor
Diameter
(m.D)
2
122
12 yyxxmin
Surrounding
area of the
polygon
(SAP)
fm cmNfm
fm
Ncm
cm
cmfmR1 1
,
Eccentricity M.D
.M.D22
Dm
Circularity 2Perimeter
area*4
Roundness 2)Diameter_Maximum(
area*4
Solidity
SAP
area
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Elongation
Diameter_Maximum
Diameter_Minor
Compactness Diameter_Maximum
area*4
Maximum
Intensity
)I(Rmax
Minor
Intensity
)I(Rmin
Ratio
Intensity Intensity_Maximum
Intensity_Minor
We show in table 3.1 the value of the
feature vector for each category of calcification
in BIRADS 1, and in table 3.2 feature vector for
each category of calcification in BIRADS 2.
Table 3.1 Feature Vector of BIRADS 1
Metrical Canes Eggshell Dystrophic Vascular
Eccentricity 0.992
0.822
Circularity
0.806
0.171
Roundness
Solidity
0.927 0.901
Elongation 0.123
Compactness
0.898
Ratio Int. 0.234 0.357
Table 3.2 Feature Vector of BIRADS 2
Metrical Sutures Pop Round Punctate
Eccentricity 0,996
Circularity 0,066
0,904
Roundness
0,049
Solidity
0,976 1
Elongation 0,085 0,848
Compactness 0,901 0,982
To recognize the patterns we use a
Backpropagation artificial neural network, The
Backpropagation (BPR) neural network has a
supervised learning method, and is a
generalization of the delta rule. It requires a
teacher that knows, or can calculate, the desired
output for any input in the training set. We show
in Fig. 3.1 the architecture of a BPR [29].
Figure 3.1 Architecture of a BPR[9].
The BPR for our case has two hidden
layers with 25 and 19 neurons respectively.
4. Methods and Results
We studied 30 cases. We compare the
resultant CAD recognition with the clinical
evaluation of the same sample. The clinical
evaluation that provides us with the medical
diagnosis was performed by the Department of
Radiology of Hospital Universitario de Puebla.
We show in the next set of figures, the
resultant recognition performed by our CAD
system, together with the clinical recognition.
According to Table 1.1 and Table 1.2, Fig. 4.1
shows a Pop Corn, Fig. 4.2 an Eggshel, Fig. 4.3
a Suture and Fig. 4.4 a Punctate type form
respectively.
Figure 4.1 Recognition of an Eggshell.
Figure 4.2 Recognition of a Suture.
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Figure 4.3 Recognition of a Pop Corn.
Figure 4.4 Recognition of a Punctate.
Note that the CAD results are in good
agreement with the clinical diagnosis.
Finally, to measure the response of the
system we use a confusion matrix, which is
shown in the table 4.1 below.
Table 4.1 Confusion Matrix
Positive
Test
True Positive
(TP)
False Positive
(FP)
Negative
Test
False Negative
(FN)
True Negative
(TN)
Total TP+FN FP+TN
The confusion matrix allow us find the
sensitivity (“positivity case”); which refers to
the proportion of subjects that give positive test
results, and the specificity (“negativity case”);
which refers to the proportion of subjects that
give negative test results. The corresponding
formulas of the sensitivity and the specificity
are given as [30].
,FNTP
TPySensitivit
(3)
and
.TNFP
TNySpecificit
(4)
The resultant values of sensitivity and
specificity for BIRADS 1 (see Table 1.1) are
included in tables 4.2 and 4.3 respectively,
while for BIRADS 2 (see Table 1.2) are
included I Tables 4.4 and 4.5, respectively.
Table 4.2 Table of Sensitivity for Birads 1.
Canes Eggshell Dystrophic Vascular
0.949 1 0.929 0.75
Table 4.3 Table of the Specificity for Birads 1.
Canes Eggshell Dystrophic Vascular
0.978 0.994 1 0.998
Table 4.4 Table of Sensitivity for Birads 2.
Sutures Pop Round Punctate
1 1 1 0.973
Table 4.5 Table of the Specificity for Birads 2.
Sutures Pop Round Punctate
0.998 1 1 0.935
5. Conclusions
We have presented a CAD system that
allows the automated image analysis to identify
calcifications in mammograms based on the
standard BIRADS 1 and 2. The proposed
BICAD system was developed using MATLAB
2010a and its Neural Network Toolbox, and was
applied to a database of digital mammography
in DICOM format provided by the Department
of Radiology, of the University Hospital of
Puebla.
We used the Difference of Gaussian
filter to find edges of the forms of the different
calcifications and a Backpropagation Artificial
Neural Network for the pattern recognition of
the BIRADS 1 and 2 cases. This method
allowed us to automate the segmentation of the
calcifications with a low computational cost.
This automated image analyzer allows
the pattern recognition with a high level of
sensitivity of 0.9629 and a specificity of 0.9920.
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The BICAD system is in current
operation in the Department of Radiology of the
University Hospital of Puebla.
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