A COMPUTER AIDED DETECTION SYSTEM FOR

MICROCALCIFICATIONS IN BREAST PHANTOM IMAGES

Bruno Barufaldi

, Sarah Soares de Oliveira

, Leonardo Vidal Batista

, Homero Schiabel

and Manuella Santos Carneiro Almeida

Departamento de Informatica, Univerisdade Federal da Paraíba, João Pessoa, Brazil

Escola de Engenharia Elétrica, Universidade de São Paulo, São Carlos, Brazil

Agência Estadual de Vigilância Sanitária da Paraíba, Governo da Paraíba, João Pessoa, Brazil

Keywords: Breast cancer, Quality control, Visual perception, Breast phantom and Microcalcifications.

Abstract: Breast cancer control represents one of the greatest challenges that public health service faces nowadays. In

order to decrease the death rate from cancer in women, the AGEVISA-PB implemented a Mammography

Quality Control Programme to improve the performance of mammographic equipment in Paraiba - Brazil.

The evaluation method of these devices is accomplished through breast phantoms that simulate structures

found on a mammogram in order to assure the quality of radiographic images. Even so, evaluation by

technicians still suffers limitations caused by the visual inspections by individuals, such as long-time

benchmarking and subjectivity. The main purpose of this research is to develop a computerised system that

analyses radiological images of phantom MAMA-CDM and correlates with human visual perception. The

results indicate that the system developed can be used as a second opinion, thus becoming a tool of great

utility in aiding medical diagnosis.

1 INTRODUCTION

Breast cancer is a malignant tumour composed of

the abnormal development of breast cells. This kind

of cancer is most common both in Brazilian and

indeed women worldwide, representing 22% of the

new cancer cases per year (INCA, 2010). When the

disease is diagnosed early in the formation of the

tumour, it can be treated more effectively, increasing

the chances of cure. The most efficacious method for

early detection of this pathology is the mammogram,

and it consists of a radiological examination to

detect breast lesions, including non palpable lesions

(Roveda Junior, 2007).

The mammogram image quality is a constant

concern for organisations and experts who face the

challenge of early breast cancer detection, in order to

save lives and reduce the aggressiveness of

treatment (Medeiros and Elias, 2007). This is

directly related to the performance of

mammographic equipment. The handling and

maintenance of mammographic equipment interfere

in the medical evaluation quality and, when it is

performed incorrectly, it can produce radiographic

films that induce misdiagnosis.

Another factor that influences breast cancer

diagnosis is the subjectivity of human interpretation

of mammographic images. This subjectivity may

result in variations in the expert analysis, producing

different reports, according to differences of visual

perception. Issues such as eyestrain, ambient light,

low image quality and radiologist inexperience, may

influence the final diagnosis (Byng et al, 1997).

To ensure the quality of mammograms in Brazil,

the Brazilian Institute of Cancer (INCA), associated

with the Brazilian Radiology Association (CBR) and

Brazilian Agency of Sanitary Surveillance

(ANVISA), has plans for the foundation of a Quality

Programme in Mammograms that will be proposed

to the Ministry of Health for countrywide

deployment. The programme methodology requires,

among other points, the monthly evaluation of a

breast phantom imaged in the mammography

services (INCA, 2011).

A local Sanitary Surveillance Agency in the

Northeast region of the country (AGEVISA-PB)

maintains a quality control programme in

mammography, which is nationally known because

of its scientific technical and social impact. The

organisations that perform mammographic

414

Barufaldi B., Soares de Oliveira S., Vidal Batista L., Schiabel H. and Santos Carneiro Almeida M..

A COMPUTER AIDED DETECTION SYSTEM FOR MICROCALCIFICATIONS IN BREAST PHANTOM IMAGES.

DOI: 10.5220/0003789704140418

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2012), pages 414-418

ISBN: 978-989-8425-89-8

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

examinations in the State of Paraiba send monthly a

phantom image to AGEVISA-PB, for the quality

evaluation of mammography services (Carvalho et

al, 2006). The procedure is arduous and time

consuming, with each phantom image carefully

analysed by experts for approximately 40 minutes.

These technicians classify each structure of interest

in the phantom by visibility criterion, producing

reports for quality image evaluations of the

mammographic equipment.

Computer Aided Detection (CAD) is used to

reduce the difficulties found in the identification of

structures in mammographic images by individuals,

providing a second opinion about the expert report.

These systems, when specific for mammographic

images, promote the integration between medicine

and technology to improve the detection in the

structures of interest (Porto, 2010).

Nevertheless, even using robust computerised

systems, detection of some structures of interest in

phantom images is still a difficult task. Because of

the smaller sizes in relation to other structures, high

contrast details can be confused as artefacts from the

revelation process of the radiographic film (Soares

and Lopes, 2001).

Artefacts in radiographic film can be related to

the processor rolls, the mammographic equipment

and the chemicals used for cleaning the equipment

or the film revelation. Mammographic images can

contain noises such as roller marks, spots,

fingerprints, silver deposits, etc.

In the adjustment activity of a radiographic film

some fixative solutions are used to dissolve the

silver deposits not removed by the reveller solution

in the previous step. These deposits are very soluble

in water and if not dissolved, can be sensitised by

the light, generating similar artefacts to the high

contrast detail on radiographic film.

This research aimed to develop a system that

automates most of the steps in the procedure of

quality control in mammographic equipment

currently adopted by AGEVISA-PB. This system

intends to detect and classify structures who

simulate microcalcifications of interest in breast

phantoms by visibility criterion, correlating with the

human visual system, in order to reduce the

subjectivity in image inspections in the phantom

MAMA-CDM (CBR, 2011).

2 MATERIALS AND METHODS

This system was developed using the Java

programming language along with the ImageJ, open

source software focused on the development of

image processing and analysis applications. The

algorithms developed were incorporated into ImageJ

through the use of plugins, and the system interface

was integrated with the system functionalities.

The system uses the Microtek scanner

ScanMaker i800 model to digitize the phantom

images in grayscale, 16 bits of contrast resolution

and 1200 x 1200 dpi of spatial resolution.

One of the approaches for visual inspection on

radiographic film is to determine the visibility of

structures of interest in the phantom MAMA-CDM

images. To detect these structures it is necessary that

the system defines different regions of search for

each phantom, due to the structure of interest

location which varies from one phantom to another

because of their handmade production.

2.1 Phantom MAMA-CDM

Breast phantoms are used to assess the quality

control of mammography services. The main

purpose of these phantoms is in the evaluation of

mammographic equipment, through the images that

contains structures which simulate the breast tissues.

The AGEVISA-PB developed a Programme of

Quality Control in Mammography, which uses the

phantom MAMA-CDM for the production of

radiological images from mammography equipment

in the State of Paraiba - Brazil. The use of this breast

phantom is recognised by the CBR (CBR, 2001). It

is interesting to know that these phantoms are

produced in a handmade mode, simulating a

compressed breast between 4 cm and 5 cm, with test

structures similar to the anatomical structures

present in the breast and a range of optical densities.

Figure 1 presents the breast phantom MAMA-

CDM, its radiographic image and the representation

of its structures. It is estimated on these images (A)

the background optical density, (B) the details of

low contrast (fibrous tissue), (C) the low contrast

thresholds (discs), (D) the high contrast details

(microcalcifications), (E) the structures who

simulate tumour masses and (F) the spatial

resolution (metal grids).

2.2 High Contrast Details

The images used have high resolution, which

eventually results in poor computer performance.

Therefore, areas of search were delimited for the

structure location in images of phantoms. From the

moment it receives an input image, the system

automatically adjusts its orientation through a

A COMPUTER AIDED DETECTION SYSTEM FOR MICROCALCIFICATIONS IN BREAST PHANTOM IMAGES

415

Figure 1: Breast phantom MAMA-CDM, its radiological image and the representation of its structures.

rotation based on the angle between the brightest

optical density and the darkest optical density. After

the adjusted image, is executed it looks for other

structures in the search regions by the correlation

matching method (Gonzales and Woods, 2002).

The visibility determination of each structure of

interest is assessed by a data mining tool called

WEKA, with the use of the J48 classifier. This

classifier will generate a model, where it is

necessary preselect some image attributes to execute

the training stage of the system (Martinez and

Sanjurjo, 2009).

In the production of each learning model, the J48

algorithm was used because of its simplicity and

satisfactory results. Moreover, in previous

researches, other algorithms have been tested to

develop the learning model, but the best results were

achieved by the J48 algorithm (Barufaldi et al,

2011).

The attributes such as average of pixel images,

variance, standard deviation, mode, average of

structure pixels, average of background pixels,

difference of structure and background averages, and

Weber Ratio were preselected for the production of

learning model. It is noteworthy that not all

attributes used in the training stage will be employed

for the classification of structures, since some of the

image characteristics are not considered relevant by

the algorithms, and they are automatically discarded

by WEKA tool. In order to define which attributes

will be used, an automatic selector

(AtributteSelection) was used, which is implemented

by the WEKA tool.

Table 1 represents the attributes extracted from

each structure also used in the training stage, where

(i,j) and p

(i,j) are the grayscale of pixels in the

inner region (structure) and outer (background) of

the filter with size w*h at position (i,j) of the image.

The filters used in the correlation operations are

composed of two parts, the inner and outer region,

illustrated in Figure 2. The inner region tries to

match the inside structure, while the outer region

tries to match the background.

Table 1: Attributes selected from the image after the

detection of the structures of interest.

Attribute Equation

Average of the structure

pixels







jip

)*(

),(





Average of the background

pixels







jip

)*(

),(





Difference of the average

grayscale of the structure

pixels and background

Δµ = µ

– µ



Weber Ratio

(GONZALES; WOODS,

2002)









A total of 100 clusters of high contrast detail

were extracted from these images for training

purposes. These structures were classified by experts

according to their visibility, i.e., if they are visible or

not. It is noteworthy that the image reports were

produced by two or more technicians, in order to

reduce the subjectivity of visual inspection,

increasing the system consistency.

In the training stage a file is created from the

default format of the WEKA (.ARFF), with all the

input data mentioned above. Then, the J48 algorithm

of the WEKA is used to generate the decision tree

for each structure of interest. To produce and test

the learning models, leave-one-out cross-validation

was used.

In the classification stage the decision trees are

implemented based on models obtained in the

training stage. One hundred images were used for

classification tests, with 500 clusters of high contrast

details analysed. It is important to note that the

images produced in the classification step are

distinct from those of training.

BIOSIGNALS 2012 - International Conference on Bio-inspired Systems and Signal Processing

416

Figure 2: Example of the filter used for the detection and

the classification of a high contrast detail of the phantom

image.

From the classification of these structures and

the comparison between the expert reports and the

reports produced by software, it is possible to

determine statistical measures such as accuracy and

efficiency.

The software behaviour is evaluated using ROC

curves for each structure of interest, where the

sensitivity and specificity of the system are verified

(Zweig and Campbell, 1993). According to the

attribute automatic selector of the WEKA, Δμ is the

attribute that was always present in all models.

Because of this, in the development of the ROC

curves the attribute values of Δμ were varied ranges

[20,000; 50,000], since this was the most important

attribute in the training stage in all structures of

interest.

3 RESULTS

The classification results using the J48 algorithm

produced the misclassification tables for each kind

of structure of interest, indicating the accuracy rates

of the classification. These measures are presented

in Table 2.

Table 2: Misclassification table of the high contrast

details.

J48 Classification

Visible

Not

Visible

Expert

Classification

Visible 0.99 0.01

Not

Visible

0.00 1.00

Table 3 presents the rates of accuracy,

sensitivity, specificity, efficiency, positive prediction

and negative prediction, and Matthews coefficient to

the classification of each structure of interest.

Table 3: Effectiveness measures of the software to the

classification of the high contrast details using the J48

algorithm.

Measure Value

Accuracy 0.9906

Sensitivity 0.9882

Specificity 1.0000

Efficiency 0.9941

Positive Prediction 0.9882

Negative Prediction 1.0000

The sensitivity values were very close to the

positive predictions. This occurs because of the high

number of high contrast details compared to the low

rate of predictive errors (false positives and false

negatives). The same applies to the specificity and

negative prediction values.

Figure 3 allows observing the behaviour of the

system to the structures classification by ROC

curves.

Figure 3: ROC Curves to the classification of the high

contrast details (AUC = 0.98).

Figure 4 shows the marking of a high contrast

detail group detected correctly even with the

presence of the artefacts in a phantom image after

the processing by system.

Figure 4: (A) High contrast detail group with the presence

of the artefacts, before the processing and (B) the same

group detected after the image processing.

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4 DISCUSSIONS AND

CONCLUSIONS

This research presents a method for localizing and

classifying, with high precision, high contrast details

clusters in phantom images. The next step of this

work consists of executing comparative tests

between the techniques presented here with

researches related to the theme.

Statistical measures of the software, which were

presented in the previous section, suggest that the

classification of the structures of interest closes with

the expert vision of the AGEVISA-PB.

Despite of the size of the structures which

simulate the microcalcifications and the possibility

of the confusion with noise, the classification of

these groups represented high success rate of the

system (99.41%). These results are due to the

reduction in size of search regions in the images in

each phantom, as well as the highest contrast of

these structures. With the well-defined boundaries of

the regions, the probability of artefacting the

artefacts and classification from the radiographic

film instead of structures of interest is reduced.

With the implementation of the system in the

AGEVISA-PB, planned for the coming months, it is

expected that the experts will learn how to use the

software and the reports generated by computer

analysis of the phantom images as an aid to the

visual inspection. Thus, part of the process for the

Quality Control in Mammography will be automated

and the subjectivity in the image evaluation may

well be reduced.

After usability tests with the experts and

improvements in the user interface, the system will

be introduced in the establishments which provide

mammography services, to execute their own quality

control in an efficient mode and with the appropriate

frequency.

ACKNOWLEDGEMENTS

CNPq, CAPES and SESU / MEC for the support in the

form of research scholarships and funding.

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