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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