Computer Methods and Programs in Biomedicine 155 (2018) 153–164

Contents lists available at ScienceDirect

Computer Methods and Programs in Biomedicine

journal homepage: www.elsevier.com/locate/cmpb

Spatiotemporal features of DCE-MRI for breast cancer diagnosis

Masood Banaie

a , Hamid Soltanian-Zadeh

a , b , ∗, Hamid-Reza Saligheh-Rad

c , Masoumeh Gity

d

a Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran,

Tehran, Iran b Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, USA c Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Science, Tehran, Iran d Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Medical Imaging Center, Imam Khomeini Hospital, Tehran University of Medical

Sciences, Tehran, Iran

a r t i c l e i n f o

Article history:

Received 25 April 2017

Revised 9 November 2017

Accepted 12 December 2017

Keywords:

Computer aided diagnosis

Breast cancer

DCE-MRI

Feature fusion

a b s t r a c t

Background and Objective: Breast cancer is a major cause of mortality among women if not treated in

early stages. Previous works developed non-invasive diagnosis methods using imaging data, focusing on

specific sets of features that can be called spatial features or temporal features. However, limited set

of features carry limited information, requiring complex classification methods to diagnose the disease.

For non-invasive diagnosis, different imaging modalities can be used. DCE-MRI is one of the best imag-

ing techniques that provides temporal information about the kinetics of the contrast agent in suspicious

lesions along with acceptable spatial resolution.

Methods: We have extracted and studied a comprehensive set of features from spatiotemporal space to

obtain maximum available information from the DCE-MRI data. Then, we have applied a feature fusion

technique to remove common information and extract a feature set with maximum information to be

used by a simple classification method. We have also implemented conventional feature selection and

classification methods and compared them with our proposed approach.

Results: Experimental results obtained from DCE-MRI data of 26 biopsy or short-term follow-up proven

patients illustrate that the proposed method outperforms alternative methods. The proposed method

achieves a classification accuracy of 99% without missing any of the malignant cases.

Conclusions: The proposed method may help physicians determine the likelihood of malignancy in breast

cancer using DCE-MRI without biopsy.

© 2017 Elsevier B.V. All rights reserved.

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. Introduction

Breast cancer is one of the diseases that are widespread in the

orld, especially in the developed countries. Breast cancer is the

ost prevalent cancer (22% of all new cases) as well as the lead-

ng cause of cancer deaths in women worldwide (14% of all cancer

eaths). On the basis of the current incidence, it is predicted that

ne in eight women in the United States will develop breast can-

er in her lifetime [1] . Consequently, development of approaches to

ffectively diagnose the disease in its early stages has been a hot

opic in recent years.

A number of studies have focused on mammography for pri-

ary assessment of the disease [2–5] . Physicians usually use mam-

ography for screening purposes to detect suspicious lesions in

he breast. Because of its ease of use and low cost, some research

∗ Corresponding author at: Radiology Image Analysis Lab., Henry Ford Health Sys-

em, One Ford Place, 2F, Detroit, MI 48202, USA.

E-mail addresses: hsoltan1@hfhs.org , hszadeh@ut.ac.ir (H. Soltanian-Zadeh).

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ttps://doi.org/10.1016/j.cmpb.2017.12.015

169-2607/© 2017 Elsevier B.V. All rights reserved.

tudies have focused on these images to reach maximum detection

ate for the breast cancer in the early stages.

Segmentation of the lesions and suspected areas in the mam-

ography images has been challenging because of the low quality

f these images [2–6] . Mammography images present limited in-

ormation about the tissue and thus are insufficient for advanced

nalyses and diagnosis.

Some of the studies have focused on ultrasound imaging, which

s dynamic and low cost [7–11] . Although ultrasound images can

nclude some dynamic information about the tissue in 3-D, in

ome cases, it is not used to characterize the passage of a contrast

gent in lesions, which is essential for determining the temporal

eatures that are based on the passage of contrast media. There-

ore, in most of the suspected cases, physicians use Magnetic Res-

nance Imaging (MRI) data for more detailed analysis.

Different studies have shown that MRI is more valuable than

ther imaging techniques for analyzing breast cancer and analysis

ethods generate superior results when using this data [12–15] .

owever, conventional MRI of the breast is less informatic in com-

154 M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164

Table 1

Parameters of the pulse sequence

used for DCR-MRI data acquisition.

Repetition Time 9 ms

Echo Time 4.2 ms

Flip Angle 30 °Field of View (FOV) 32–40 cm

Matrix Size 512 × 512

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parison with specialized dynamic imaging methods such as Dy-

namic Contrast Enhanced MRI (DCE-MRI) [16] .

Nowadays, Dynamic Contrast Enhanced MRI (DCE-MRI) plays an

important role in the diagnosis of breast cancer and is analyzed

by novel methods to help physicians diagnose breast cancer [17–

20] . Because of the rich information supplied by DCE-MRI, a major

portion of studies in this field has focused on predicting response

of the disease to treatments such as neoadjuvant chemotherapy

[21–36] .

Depending on the required precision and the allowed imaging

time, DCE-MRI is acquired in a number of phases. It includes a pre-

contrast set of images that allows tracking of the changes in the

wash-in and washout behavior of the contrast agent in the breast

tissue during the time that the images are acquired.

Different studies have used different parts of the information

that exist in the acquired data. They have extracted different fea-

tures from the data and used various classification techniques to

provide physicians with the best primary diagnosis results. Dif-

ferent CAD systems have been developed and evaluated based on

these studies to ease the diagnosis process for physicians [37–39] .

Some of the studies use semi-quantitative methods to ana-

lyze the DCE-MRI information. However, the methods mostly lack

reproductively [24,40] and are thus inappropriate for computer-

ized diagnosis systems. Recent studies have mainly focused on the

quantification of the features described in the reference medical

texts, which are based on the physicians’ experiences.

The imaging features can be divided into two main groups: spa-

tial features and temporal features. Spatial features have been de-

veloped for static (single) images. The approaches for extracting

these features have evolved over the years. These features include

different mathematical descriptors for the borders and shapes of

the suspected lesions. Some of these descriptions consider Breast

Imaging Reporting and Data System (BI-RADS). BI-RADS was origi-

nally developed for mammography to standardize the reporting of

the severity of findings. American College of Radiology (ACR) pub-

lished and trademarked BI-RADS based on the requests and opin-

ions of the researches working in the field. It may be used by doc-

tors to specify a patient’s risk of developing breast cancer [41] . The

segmentation quality affects the quality of the spatial features.

Temporal features focus on dynamic behavior of the contrast

agent in the lesions during consecutive recordings. It has been

shown that for accurate modelling of the kinetic behavior of the

lesions, the time difference between consecutive recordings should

be 16 sec or less [42] . This acquisition time would make the spa-

tial resolution of the images very low. Therefore, some studies –

including current study – have used the temporal features of the

images acquired in higher acquisition times for the diagnosis of

the breast cancer [25,43–45] .

Using the information available in each feature space, an appro-

priate feature fusion method that employs the information from

both of the feature spaces can help with the classification process

and improve diagnosis accuracy. In this study, we use the informa-

tion present in the dynamic images to diagnose the breast cancer

in an effective manner. We use images of the cases that were ini-

tially suspected to be malignant. However, according to the biopsy

results, some of these cases were eventually diagnosed as malig-

nant or benign and the remaining cases were diagnosed as benign

based on timely follow-ups. About two thirds of the cases were

reported to have marginal diagnosis when the experts evaluated

their imaging data. The database includes some false positive cases.

The main contributions of this study are as follows:

• Extracting a comprehensive set of features including the fea-

tures proposed in the previous studies in the spatial or tempo-

ral spaces, from a balanced dataset of patients (equal number

of cases in each group).

• Proposing a fusion method to fulfill both reduction and priori-

tization of the features in a nonlinear approach to avoid infor-

mation repetition in the mixture of the two spaces along with

optimal use of information present in each space. • Achieving an accuracy rate higher than those of the existing

methods for the separation of benign and malignant breast can-

cers using DCE-MRI.

Section 2 discusses characteristics of the database, methods of

eature extraction and fusion along with classification methods.

he results of the proposed method in comparison with previous

tudies are discussed in Section 3 . A discussion of the results and

he proposed method is provided in Section 4 . Section 5 concludes

he study.

. Materials and methods

.1. Database

The dataset included the DCE-MRI data of 26 biopsy or short-

erm follow-up proven patients. The wide range of age from 30

o 69 provided a balanced dataset for our research. The average

ge of the two groups was similar (45.94 for benign and 45.54 for

alignant cases).

Some patients had more than one lesion eligible for the analy-

is. Then, the dataset included 16 benign lesions and 16 malignant

esions. Two sample images from the dataset are shown in Fig. 1 .

A large portion of the previous studies used physician’s reports,

hich were based on qualitative assessment of the images. When

e compared the reports and final diagnosis of the patients, we

ound diagnosis differences for patients with BIRADS 3 and 4 clas-

ification. Although the final diagnosis based on biopsy did not al-

ays match the initial report, none of the patients were excluded

rom the study but grouping of the patients was done based on the

nal diagnosis.

Based on the pathology reports of the patients (excluding those

iagnosed based on a 6 months follow-up), the masses included in

he study were diagnosed as:

• Benign masses, including fibroadenoma, non-proliferative fi-

brocystic changes, adenosis, intraductal papillomas, and duct

ectasis. • Malignant masses, including invasive ductal carcinoma, and in-

vasive lobular carcinoma.

.2. Data acquisition

The images were acquired using a 1.5 Tesla MRl system that

ad a dedicated bipolar phased-array breast coil (GE Healthcare,

ilwaukee, WI, USA). A T1-weighted 3D spoiled gradient-recalled

cho pulse sequence was used to acquire the DCE-MRI of the

reast. The parameters of the pulse sequence are listed in Table 1 .

Six series of images, one pre-contrast and five post-contrast,

ere acquired. Each dynamic acquisition series had a temporal res-

lution of about 80 s (varies slightly depending on the patient’s

reast size) and an average of 64 axial slices. Before the injection

f the contrast agent, the first (pre-contrast) series was acquired.

M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164 155

Fig. 1. A slice of DCE-MRI from two different patients showing suspected lesions.

Fig. 2. Sample images of dynamic changes of the contrast for three benign lesions.

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hen, 0.2 mmol/Kg of Gd-DTPA was injected manually and intra-

enously following by 20 cc normal saline. Next, five post-contrast

eries were acquired. Figs. 2 and 3 show sample images of the

reast with a benign or malignant lesion, acquired in consequent

cquisition times.

.3. Feature extraction

We extracted the features from the suspected lesions in the im-

ges, then, a specialist marked the lesions on the post-contrast im-

ges. We used a technique based on an initial point selection in-

ide the lesion. Considering a specific lesion, all the pixel intensi-

ies inside the lesion borders belong to a specific range of inten-

ities. Our method finds the pixels of the image that are brighter

han the threshold computed using the initial point. Then, for le-

ion segmentation, we extracted the connected component that in-

luded the initial point. Finally, the border of the segmented lesion

as shown (see Fig. 4 ). Extracted borders were double-checked by

physician to validate the segmentation results.

.3.1. Spatial features

The extracted lesions are processed using different methods to

uantify their shapes and textures. These features represent the

haracteristics usually used by physicians to determine the nature

f different lesions in the breast images. These characteristics are

ostly discussed in Breast Imaging Reporting And Data System (BI-

ADS) which is a widely accepted risk assessment and quality as-

urance tool in mammography, ultrasound, and MRI. The system

as been designed to make the reporting of mammograms stan-

ard and comprehensible to the non-radiologist reading the report

41] . A system for automatic risk assessment based on the com-

uterizing the described features of the lesions may help physi-

ians by increasing the precision for the diagnosis process. In this

tudy, different feature sets that can describe different spatial as-

156 M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164

Fig. 3. Sample images of dynamic changes of the contrast for three malignant lesions.

Fig. 4. Segmentation results for the sample images of Fig. 1 . The border of the segmented region is shown using a thick white line.

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pects of the lesion are considered. Then, a set of important fea-

tures for diagnosis of cancer based on BIRADS that includes all the

information about the lesion is selected. The features extracted in

this section quantify the shape of the segmented lesion. These fea-

tures consider the smoothness of the border and the regularity of

the shape. Based on the BIRADS descriptors for the severity of the

lesion, it can be noted that irregular masses are more probable

to be cancerous [41,46] . In addition, the system includes descrip-

tors based on the dynamic behavior of the enhancement (temporal

features).

Texture features are extracted based on the Gray-Level Co-

Occurrence Matrix (GLCM) [47–49] . These features include autocor-

relation, sum of squares, sum average and variance, difference vari-

ance and entropy, information measure of correlation, inverse dif-

ference, inverse difference normalized, inverse difference moment

normalized, uniformity, energy, and angular second moment.

Shape features including Fourier factor for farthest point and

radial length are added to the texture feature space to complete

i

he spatial features. Farthest point distance is calculated as:

P D ( u ) =

√

( x ( u ) − x c ) 2 + ( y ( u ) − y c )

2

+

√ (x f p ( u ) − x c

)2 +

(y f p ( u ) − y c

)2

here ( x fp (u), y fp ( u )) is the farthest point and ( x c , y c ) is the center

oint as shown on a sample lesion in Fig. 5 . This feature is related

o the shape of the lesion and is a measure of the irregularity of

he border, which usually increases in cancerous lesions.

Consequently, the spatial feature set include 51 features (3

hape features and 48 texture features) that represent the spatial

haracteristics of the lesion.

.3.2. Temporal features

Temporal Features can be calculated using model-based and

odel-free methods. Both methods have been used for analyz-

ng DCE-MRI of breast cancer [50] . Model-free methods consider

M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164 157

Fig. 5. An illustration of the center point and farthest point. Here, (x c ,y c ) is the

coordinates of the center point and (x fp ,y fp ) is the coordinates of the farthest point

for a sample lesion.

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escriptions that are mentioned in BIRADS [46] . They are semi-

uantitative and subjective and thus depend on the quantification

ethod used. The parameters of the models vary based on the as-

umptions made. Previous studies have shown that there is a good

orrelation between model-based and model-free methods [51] . In

his study, we include all features that may extract dynamic in-

ormation of the data. A set of features are extracted by fitting

he standard and modified Tofts models [52,53] to the dynamic

hanges in the lesion. The Tofts model is a compartmental model

hat considers blood vessels and the tissue as two connected com-

artments. The formulation of this model is:

t ( t ) = K

trans

∫ C p ( τ ) e −k ep ( t−τ ) dτ

here C t is the tracer concentration in the tissue, K

trans is a con-

tant value called transfer constant, C p is the tracer concentration

n the arterial blood plasma, and k ep is the rate constant. The equa-

ion is based on a general model for transfer between two com-

artments: one with high density of a matter and the other with

ow density of the same matter. Most of the studies using this fea-

ure extraction method consider K

trans as a feature, which plays an

mportant role in the classification.

For a better understanding of the idea behind the Tofts model,

graphical illustration is shown in Fig. 6 .

ig. 6. An illustration of how the Gd-DTPA contrast gets in and out of a breast lesion. The

esion is considered in the models.

The Modified-Tofts (M-Tofts) tracer kinetic model (standard

ofts model plus plasma volume, vp) is often used to analyze DCE-

RI data. It represents a particular case of the two-compartment

xchange model with two simplifying assumptions generally: (1)

egligible plasma mean transit time (MTT); and (2) plasma flow

Fp) much greater than microvascular permeability-surface area

PS). The two-compartment exchange model provides separate es-

imates of PS and Fp, while the M-Tofts model only provides an

stimate of the transfer constant, K

trans . K

trans reflects PS if the sim-

lifying assumptions are met and it may be sensitive to Fp if they

re not met.

In addition, to enrich the feature set, additional features are ex-

racted by fitting the data to the Brix model [54] . The features ex-

racted from the model parameters can be effective in the classi-

cation process. In the model, the signal enhancement is assumed

o be proportional to the CA concentration in the tissue. Then, the

lasma concentration is fitted with single exponential decay char-

cterized by a rate constant k el . The Brix model formula is:

( t.A. k el . k ep ) =

A

k el − k ep

(1 − exp ( −k ep t )

k ep − 1 − exp ( −k el t )

k el

)

here A is the initial slope of the curve.

To optimize the information extracted from the dynamic im-

ges, we have included the parameters extracted from the Adia-

atic approximation of the Tissue Homogeneity (ATH) model in the

eature set. This approximation assumes that the tracer concentra-

ion in parenchymal tissue changes slowly relative to that in cap-

llaries. Based on these assumptions, a time-domain, closed-form

olution of the tissue homogeneity model is derived. The form of

he solution, which is usually called the adiabatic solution, is sim-

lar to those of two-compartment models [55,56] .

The model is more complex than the previous models. It as-

umes that the concentration of CA in the tissue ( C t ( t )) is equal to

he convolution of the arterial input function ( C p ( t )) with the fol-

owing model-based impulse response function ( H ( t )):

( t ) =

⎧ ⎨

⎩

F P t ≤ T c

E · F P exp

(−E · F P

v e ( t − T c )

)t > T c

The model considers different parameters including: plasma

ow rate ( F p ), extraction fraction through first-passage ( E ), mean

contrast agent is injected to the veins. The exchange of CA between veins and the

158 M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164

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capillary transit time ( T c ), and interstitial volume fraction ( v e ). The

parameters described above are related as follows:

K

trans = E · F

v p = T c · F

The model parameters are selected as features and their values

evaluated for classification.

Some recent studies showed that a centripetal/centrifugal en-

hancement pattern was valuable in the diagnosis process [57] .

Therefore, this feature was also included in the pool of features.

In this study, the feature for type of enhancement was consid-

ered in 5 levels. A large portion of the patients could not be val-

ued as centrifugal or centripetal by the physicians. Then, a state

named none is considered for these patients. Moreover, to make

this feature more informative for the classification process, each

enhancement group was also divided into two sets based on their

enhancement level.

Finally, the feature set includes the following 22 features ex-

tracted from the temporal (dynamic) information of the images.

• k Trans using Modified Tofts calculating AIF • kep using Modified Tofts calculating AIF • k Trans using Modified Tofts • kep using Modified Tofts • vp using Modified Tofts • A using Brix Model • kep using Brix Model • kel using Brix Model • Tc using ATH Model • E using ATH Model • ve using ATH Model • tc using Schabel Model and Parker AIF • alfa using Schabel Model and Parker AIF • E using Schabel Model and Parker AIF • F using Schabel Model and Parker AIF • tc using Schabel Model and Weinmann AIF • alfa using Schabel Model and Weinmann AIF • kep using Schabel Model and Weinmann AIF • F using Schabel Model and Weinmann AIF • Cenrtifugal Features • k Trans using Standard Tofts • kep using Standard Tofts

Fig. 7 shows a tree representation of the feature types used as

the primary features.

2.4. Feature fusion

Various studies have focused on using features from different

domains to improve the classification process [58–62] . Most of the

studies on breast cancer diagnosis use computational approaches

and focus on different classification methods to reach the best di-

agnosis rate using spatial or temporal features. These studies were

reviewed in previous sections of this paper. Since the spatial and

temporal features include different information, using both feature

spaces can increase accuracy of the diagnosis process. In this study,

we applied different techniques to fuse the mentioned features. In

addition, since feature reduction is another target for the process

and different studies in this subject have used SVM for the clas-

sification process, Kernel Principal Component Analysis (KPCA) is

used for this purpose. KPCA uses kernel methods to extend Prin-

cipal Component Analysis (PCA). This extension allows the linear

operations of PCA be done in a reproducing kernel Hilbert space

with a non-linear mapping. The principal components may be ef-

ficiently computed in high dimensional feature spaces that are re-

lated to the input space by some nonlinear maps [63] . Some previ-

us studies have used this method to improve discriminating abil-

ty of the features [64–66] . Fig. 8 provides a schematic comparison

f the linear PCA and Kernel PCA methods.

This method would find a suitable kernel function for the

ataset. Then, it would decrease the dimension of the feature set

hich would decrease the classifier complexity. Decreasing the

lassification complexity is a main advantage of this method. Be-

ause the number of patients in the dataset is limited, in this stage

f the study, we decreased the number of features to four. The four

eatures in 2-D spaces are shown in Fig. 9 . These features are the

esult of a mapping that combines the information of the input set

f features and outputs new features with effective discriminative

alue. However, if the size and variety of the information in the

ataset increase, a higher dimensional feature space is needed to

nclude all discriminative information.

.5. Classification

In this stage of the study, we applied different classification

ethods. Previous studies have used complex classification meth-

ds but this study simplifies the classification stage. We use a sim-

le feed-forward neural network with three neurons in the hidden

ayer. Moreover, the output of the neural network is rounded to

he nearest integer to achieve results that are comparable with the

arget values. The network classifies the input data, which includes

he features of the two groups (malignant and benign).

We have also developed a reference/conventional method based

n the techniques widely used in the literature. To this end, we re-

uced the input feature set to four features using a straightforward

eature reduction method. This made it possible to obtain a gener-

lizable classification by preventing over-fitting. We then used a

upport Vector Machine (SVM) to classify the reduced feature set.

To avoid overfitting, we separated the test and train sets before

he fusion stage. This means that the trained classifier was evalu-

ted on the test data, which was not used in the training of either

he fusion or the classification methods.

. Results

Features from the spatial and temporal spaces were selected

onsidering those used in the previous studies. In this section, we

rst present a primary statistical analysis to determine the effec-

iveness of the individual features for benign and malignant lesions

lassification. Then, we present the classification results using all of

he spatial and temporal features. Next, we describe the classifica-

ion results of the spatial features alone or the temporal features

lone.

.1. Evaluation of effectiveness of individual features

To evaluate the effectiveness of a feature to differentiate the

wo groups, we used two approaches: (1) class discrimination and

2) correlation with the class labels.

In the first approach, we applied a two-sample t -test. This test

s used to statistically evaluate the difference between two popu-

ation means when standard deviations are unknown and samples

re drawn independently. When the sample size is limited, this test

orks best if the data are drawn from normal or close to normal

istributions. As expected, the confidence in the results increases

s the sample sizes increase [67,68] .

In our test, the null-hypothesis assumes that the data of the

wo groups are from independent random samples of normal dis-

ributions with equal means and unknown variances. Considering

p -value of .01, the null hypothesis was rejected in 16 of the spa-

ial features and 7 of the temporal features, i.e. , these features may

iscriminate the two groups. The results illustrate that there are

M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164 159

Fig. 7. Groups of features used in the study.

Fig. 8. (a) Linear PCA computes the projections of the features onto the eigenvectors of the covariance matrix. Linear PCA uses the eigenvectors of the covariance matrix to

generate an orthogonal transformation of the coordinate system. (b) kernel PCA maps the input feature space by a nonlinear transformation M. This transforms the contour

lines of constant projections onto the principal eigenvector to curves in the input feature space. Green crosses show the data samples. (For interpretation of the references

to color in this figure legend, the reader is referred to the web version of this article.)

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everal potentially discriminating features, which need to be ana-

yzed using feature selection, feature combination, and classifica-

ion methods.

Table 2 shows the list of the top-ten features selected by the

-test along with their associated p-values, their ranks in the pro-

osed feature fusion approach, and a list of other studies that have

sed the same features for the classification of similar datasets.

In the second approach, we estimated the correlation of each

eature with the class labels ( −1 for the benign and + 1 for the ma-

ignant cases). Interestingly, nine out of the top-ten features were

160 M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164

Fig. 9. The four resulting features, shown in 2-D feature spaces.

Table 2

Top-ten features selected by the t -test, their associated p -values, their rank in the proposed feature fusion approach, and a list of other studies that have

used the same features.

Feature Name p -value Rank in 1st Rank in 2nd Rank in 3rd Rank in 4th Other studies that used

fused feature fused feature fused feature fused feature the feature

Correlation 2 from GLCM 2 p < 10 −7 68 48 9 70 [47–49]

Cluster Prominence from GLCM 1 p < 10 −6 67 11 16 53 [47–49]

Cenrtifugal Features p < 10 −6 9 3 4 10 [24,33,40,57]

Fourier Factor of Radial length p < 10 −5 18 50 50 18 [57,64]

Correlation 1 from GLCM 1 p < 10 −4 70 9 13 2 [4,47–49]

Correlation 2 from GLCM 1 p < 10 −4 69 54 12 37 [4,47–49]

Normalized Radial Length 5 (Entropy) < .0 0 04 34 14 21 21 [18,57]

Fourier Factor of Farthest Point < .0 0 04 21 21 66 39 [57,64]

Homogeneity from GLCM 1 < .002 36 20 48 68 [47,49]

F using Schabel Model and Weinmann AIF < .003 19 53 30 72 [36,43,44,50]

Table 3

Top-ten features with largest correlations with the class labels, their correlation values, and their rank in the proposed

feature fusion approach.

Feature name Correlation Rank in 1st Rank in 2nd Rank in 3rd Rank in 4th

coefficient fused fused fused fused

feature feature feature feature

Correlation 2 from GLCM 2 0.65 68 48 9 70

Cluster Prominence from GLCM 1 0.59 67 11 16 53

Centrifugal Features 0.57 9 3 4 10

Fourier Factor of Radial length 0.53 18 50 50 18

Correlation 1 from GLCM 1 0.38 70 9 13 2

Correlation 2 from GLCM 1 0.38 69 54 12 37

Normalized Radial Length 5 (Entropy) 0.33 34 14 21 21

Fourier Factor of Farthest Point 0.32 21 21 66 39

F using Schabel Model and Weinmann AIF 0.24 19 53 30 72

tc using Schabel Model and Parker AIF 0.23 15 67 25 63

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the same as the top-ten features selected by the t -test. Table 3

shows the list of the top-ten features selected by the correlation

analysis along with their correlation values and their ranks in the

proposed feature fusion approach.

It should be noted that the results of the above individual fea-

ture evaluation methods do not mean that other features do not

include any useful information for the classification of the sam-

ples. These results mean that the features used individually include

more relevant information than other features. Consequently, when

multi-dimensional feature spaces are explored, while the top-ten

features are still used as one of the top-ranking features, their con-

c

ributions (ranks) would be different from their individual ranks as

llustrated in Tables 2 and 3 .

.2. Validation of dataset and features

To validate our dataset and the extracted features, we have

dentified three previous studies [11,15,69] that have used a sub-

et of our proposed features. Due to restrictions in accessing the

atasets used by these studies, we have applied their classification

ethods to our dataset. The results obtained by our method are

ompared with the results of the original studies in Table 4 .

M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164 161

Table 4

Application of three approaches (features and classification methods) used in similar studies to our dataset.

Study Number of cases used in the original study Number of features Reported accuracy Accuracy using our dataset (%)

Tsui et al. [11] 60 5 80.00% (Best result) 78.12

Huang et al. [15] 82 (44 benign, 51 malignant) 22 88.42% 90.63

Honda et al. [69] 90 (28 benign, 62 malignant) 11 85.6% 87.5

.

Input layer (4 Features ) . Output Layer (Diagnosis)

Fig. 10. The neural network structure used for classification.

Segmenta�on

Feature Extrac�on: Temporal,

Spa�al

Feature Fusion (KPCA)

Classifica�on: Benign,

Malignant

Fig. 11. Schema of the diagnosis process.

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The results listed in Table 4 indicate that the dataset and fea-

ures we used in this study are consistent with those used by the

revious studies and can obtain accuracy results that are similar to

hose reported by the other groups.

.3. Comparison of results of proposed methods using spatial

eatures, temporal features, and both

According to the details discussed in previous sections, two dif-

erent approaches are used to diagnose the disease and the results

re compared. The process starts with features extracted to repre-

ent spatial and temporal characteristics of the lesions. The initial

eature set includes 73 features. This feature set is then fed into

he feature fusion stage to obtain 4 features that carry the main

art of the information for diagnosis. This process is done on only

he training set in each process to avoid overfitting. The resulting

eature set is used for the classification process. The classifier is

neural network with three neurons in the hidden layer. Fig. 10

hows a schematic of the structure of the neural network.

The schema of the complete process is shown in Fig. 11 . The

rocess is run on a personal computer with an Intel ® Core TM i7

rocessor and 8 GB of RAM. The simplicity of the classification pro-

ess in the proposed method decreased the time of a single chain

f the process, including the feature extraction process, to less than

0 min.

To compare the proposed method with the methods discussed

n the previous studies, we developed a parallel method that in-

ludes a conventional feature reduction method along with a SVM

lassifier. We selected this classifier because it is a common clas-

ifier used in similar studies. In addition, to evaluate the vari-

nce or the confidence interval of the estimated performance mea-

ures (accuracy, true positive, true negative, false positive, and false

egative rates), we randomly selected 50 subsets of the original

ataset including 15 benign and 15 malignant lesions and applied

he classification methods. The means and standard deviations of

he resulting performance measures are listed in Table 5 . This ta-

le illustrates the superiority of the proposed method relative to

he reference method. It is notable that the false negative rate of

he proposed method is zero for all of the 50 data subsets, mean-

ng that our proposed method did not misclassify any of the ma-

ignant cases.

Next, to evaluate the roles of the spatial and temporal features

n classification, we applied both of the proposed method and the

eference method to these features separately.

We used the spatial features first. The results are presented in

able 6 . This table shows the spatial features, which generate di-

gnosis rates lower than those generated by the proposed compre-

ensive set of features.

Next, we used the temporal features. The results are presented

n Table 7 , showing the inferiority of the results to the above clas-

ifications that use the spatial features or both of the spatial and

emporal features.

. Discussion

Because of the risks and difficulties of the invasive diagnosis

ethods, diagnosis of the breast cancer using non-invasive meth-

ds has been a hot topic in recent years. However, none of the pro-

osed methods could guarantee a completely accurate diagnosis.

s discussed in the introduction section, physicians and engineers

ave worked on different imaging techniques along with different

rocessing approaches to reach accurate diagnosis systems. Mam-

ography and ultrasound images are mostly used only for prelim-

nary analysis. However, the quality of images is low and do not

nclude enough information about the lesions for the accurate di-

gnose and detection of their cancerous nature. DCE-MRI can pro-

ide a set of high-quality images from the lesions that include not

nly useful information about the shape of the suspected lesions

ut also useful information about the dynamic behavior of the con-

rast agent in the lesion. This dynamic information along with the

patial information about the lesion can make accurate diagnosis

ossible.

Most of the previous works in this field focus on one of the spa-

ial or temporal feature spaces. They sometimes include features

rom both spaces but focus on one of them as the key features.

ecause the nature of these two spaces is different, they include

omplementary information to improve the diagnosis process.

In 2008, Ertas et al. [17] studied segmentation and lesion de-

ection techniques for MR images using cellular neural networks

162 M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164

Table 5

Comparison of the results of the proposed method and the reference method that uses a conventional feature reduction method along with

a SVM classifier.

Accuracy False positive rate True positive rate False negative rate True negative rate

Proposed fusion based method 98.87 ± 0.25% 2.13 ± 0.89% 97.88 ± 0.89% 0.00 ± 0.00% 10 0.0 0 ± 0.00%

Standard SVM based method 93.07 ± 0.49% 3.79 ± 2.10% 96.21 ± 2.10% 9.22 ± 1.20% 90.78 ± 1.20%

Table 6

Comparison of the results obtained using the spatial features only.

Accuracy False positive rate True positive rate False negative rate True negative rate

Proposed fusion based method 89.25 ± 0.16% 8.06 ± 0.35% 91.94 ± 0.35% 11.63 ± 0.20% 88.37 ± 0.20%

Standard SVM based method 91.00 ± 0.64% 13.24 ± 0.52% 86.76 ± 0.52% 3.53% ± 1.4% 96.47 ± 1.4%

Table 7

Comparison of the results obtained using the temporal features only.

Accuracy False positive rate True positive rate False negative rate True negative rate

Proposed fusion based method 72.93 ± 1.20% 30.81 ± 0.67% 69.19 ± 0.67% 20.46 ± 3.30% 79.54 ± 3.30%

Standard SVM based method 66.80 ± 0.59% 33.87 ± 0.53% 66.13 ± 0.53% 32.40 ± 0.82% 67.60 ± 0.82%

t

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and 3D template matching. They studied a dataset of images from

19 women and gained 97% correct segmentation. In 2012, Pang

et al. [18] used a two-step method, including fuzzy c-means and

snake algorithm, to segment MR images. They studied 60 mass-

like lesions and reached 97% correct segmentation. These stud-

ies confirm that recent segmentation techniques are not ideal and

the correct segmentation of the suspected lesions is still a chal-

lenge. Consequently, the classification methods that use the re-

sults of these methods might be inaccurate. Therefore, this study

avoided using fully automatic segmentation methods. In this study,

a semi-automatic method was used for the segmentation of the le-

sions. This method worked based on selecting an initial point by a

trained radiologist.

Some recent studies focused on dynamic features of enhance-

ment to determine the nature of the lesions. Ko et al. [19] exam-

ined some basic features for this purpose. They found a feature

called signal enhancement ratio that included meaningful infor-

mation about the lesion. Other studies focused on different types

of features. Yi et al. [51] focused on the correlation between the

model-based perfusion parameters and model-free parameters of

time-signal intensity curve of dynamic contrast enhanced MRI in

breast cancer patients. They found significant correlations among

the model-based and model-free parameters. This shows that fea-

tures extracted from same (dynamic) space would include almost

same information. Therefore, this study has focused on different

model based dynamic features to generate a relatively complete set

of dynamic features.

In this study, we used the most suitable features to obtain the

best classification of the benign and malignant lesions using dy-

namic MRI images of the breast. They included a comprehensive

set of spatial and temporal features. A statistical t -test and a corre-

lation analysis were used to evaluate the capability of the individ-

ual features in discriminating the benign and malignant cases. This

was done for obtaining insight into the usefulness of the features.

Then, all extracted features were combined using the proposed fu-

sion method.

The result of combination (fusion) of the proposed feature set

helped the classification methods achieve high classification rates.

The results of the proposed method showed a small false positive

rate with no false negatives. This is an indication that the proposed

features included sufficient information about the lesions. More-

over, since the results included false positives only, the proposed

method can serve as a diagnosis aid for the physicians. In addi-

tion, the proposed feature fusion method generated the features

s

hat simplified the classification process. The proposed classifica-

ion method used a three-layer feed-forward neural network with

hree neurons in the hidden layer.

After verifying the validity and ability of the proposed method,

he spatial and temporal features were evaluated separately. This

valuation showed that the results of none of these feature spaces

as comparable to the feature space generated by the integration

f the two spaces. It also showed that the spatial features outper-

ormed the temporal features.

Image quality naturally affects the image analysis results. To

his end, the accuracy of the proposed methods depends on the

uality of the information extracted from the DCE-MRI data. Noise

ecreases the quality of this information and thus deteriorates the

obustness of the methods. However, the proposed method is not

upposed to be highly sensitive to noise since it uses multiple spa-

iotemporal features and noise does not equally affect all features.

or example, the centrifugal/centripetal feature is minimally af-

ected by noise. Consequently, the accuracy of the proposed system

s expected to remain higher than the methods that use a small

umber of features in a single domain.

In future studies, we will compare the effect on noise on dif-

erent methods. We will also extend the proposed approach by ex-

ending the dataset and the feature set to achieve a robust and

ully automatic diagnosis method.

. Conclusion

Using information from different aspects of a disease can help

hysicians make an accurate diagnosis. This idea holds true for

omputerized diagnosis systems too. Because of the complexity of

alculating different types of features, most of the previous stud-

es focused on one aspect and tried to reach a correct diagnosis

hrough different classification methods. This study showed that

sing a comprehensive set of features extracted from the spatial

nd temporal information of the dynamic MRI images of the breast

esions and fusing the feature spaces in a smart manner avoided

omplex classification methods while generating an accurate diag-

osis. Moreover, the studies conducted on the proposed features

sing the methods used in the literature showed that selecting the

ight set of features played a crucial role in the process.

According to the results of the study, the proposed method may

erve as a solution for achieving an accurate computer-aided di-

gnosis system. However, it should be applied on a large dataset

o evaluate the robustness of the proposed methods for an actual

ystem.

M. Banaie et al. / Computer Methods and Programs in Biomedicine 155 (2018) 153–164 163

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onflict of interest

The authors declare no conflict of interest.

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