Automated Breast Mass Segmentation using Pulse-Coupled Neural

Network and Distance Regularized Level Set Evolution: A

Coarse-to-ﬁne Approach

Songlin Du, Yaping Yan and Yide Ma

School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China

Keywords:

Breast Mass Segmentation, Pulse-Coupled Neural Network, Distance Regularized Level Set Evolution.

Abstract:

Motivation: Computer-aided diagnosis (CAD) is an important means for the clinical detection of breast cancer.

Mass is a common manifestation of breast cancer. This work aims to develop an effective breast mass segmen-

tation algorithm for CAD systems. Method: On one hand, pulse-coupled neural network (PCNN) and level set

(LS) method have complementary advantages in image segmentation, we therefore combine PCNN and LS.

On the other hand, traditional LS method formulates the evolution of the contour through the evolution of a

level set function (LSF), and LSF typically develops irregularities during its evolution, which may cause nu-

merical errors and eventually destroy the stability of the evolution. So we use an improved LS model, named

distance regularized level set evolution (DRLSE), to achieve desirable segmentation performance. Speciﬁ-

cally, we extract the region of interest (ROI) with PCNN and sets initial contour for DRLSE ﬁrst. Then the

ﬁnely segmentation is achieved by DRLSE. Results: Both qualitative and quantitative experiments on three

large-scale mammography databases prove that the proposed method achieves high segmentation accuracy.

Conclusion: The proposed algorithm is effective for automatic breast mass segmentation. Signiﬁcance: First,

the sketchy position of mass is ﬁxed by PCNN, which guides the algorithm to deﬁne a ﬂexibly initial contour

for DRLSE. This strategy makes it easier for the contour to move from initial position towards the bound-

ary between mass and normal tissue. Second, the use of DRLSE, which introduces an intrinsic capability of

maintaining regularity of the LSF, ensures stable LS evolution and achieves accurate segmentation.

1 INTRODUCTION

1.1 Background and Motivation

Breast cancer is one of the most common cancers

among women, and the key action to reduce the death

rate due to breast cancer is early detection (Liang

et al., 2012). Mammography screening, which is a

speciﬁc technique of imaging that uses a low-dose

X-ray system and high-contrast, high-resolution ﬁlm

for breast examination, can depict most of the sig-

niﬁcant abnormal tissues in breast. Computer-aided

diagnosis (CAD) techniques in mammography serve

as a second opinion for radiologists by identifying

regions with high suspicion of malignancy (Astley,

2004). Breast mass is a common manifestation of

breast cancer that continues to challenge both radi-

ologists and CAD systems (Eltonsy et al., 2007; Guo

et al., 2015). However, accurate detection of breast

mass is challenging for its complex topological struc-

tures and heterogeneous intensity distributions (Cui

et al., 2009). The task of breast mass detection is

essentially a problem of image segmentation. Im-

age segmentation studies how to partition an image

into several meaningful parts, often consisting of ob-

jects and backgrounds (Zhang and Ji, 2011; Du et al.,

2014). As an important technique in many computer

vision related applications, it is of general interest

to design robust and fast segmentation algorithms.

However, it is widely accepted that there is no gen-

eral method for solving all segmentation problems.

On the contrary, to achieve satisfactory performances,

algorithms should be highly adapted to their corre-

sponding applications. In recent years, plenty of im-

age segmentation algorithms which were developed

in the ﬁeld of computer vision have been introduced

to the detection and the segmentation of breast le-

sions. For example, Z. Wang et al. (Wang et al.,

2014) proposed to combine wavelet modulus max-

ima transform, morphological operation, and region

grow for breast tumor edge segmentation. L. Moraru

et al. (Moraru et al., 2014) transferred the problem

Du, S., Yan, Y. and Ma, Y.

Automated Breast Mass Segmentation using Pulse-Coupled Neural Network and Distance Regularized Level Set Evolution: A Coarse-to-ﬁne Approach.

DOI: 10.5220/0005621300170024

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 2: BIOIMAGING, pages 17-24

ISBN: 978-989-758-170-0

of breast lesion segmentation into extracting texture

features. R. Rouhi et al. (Rouhi et al., 2015) em-

ployed region growing and cellular neural network

(CNN) segmentation for benign and malignant breast

tumors classiﬁcation. W. Xie et al. (Xie et al., 2016)

employed level set and extreme learning machine for

breast mass classiﬁcation. In this paper, we studied

the problem of partitioning breast mass in mammog-

raphy, and proposed a coarse-to-ﬁne method which

combines pulse-coupled neural network (PCNN) and

distance regularized level set evolution (DRLSE).

1.2 Our Contribution

The PCNN, which has fundamental advantages in im-

age processing because of its biological background,

was constructed by R. Eckhorn et al. (Eckhorn et al.,

1989; Eckhorn et al., 1990) to simulate the syn-

chronous pulse bursts in cat visual cortex. Thus far, a

large plenty of literatures aiming at the applications of

PCNN have been published, such as image segmen-

tation (Kuntimad and Ranganath, 1999), image fu-

sion (Li et al., 2006), and edge detection (Zhou et al.,

2008). However, the strength of PCNN lies in ﬁnding

sketchy positions of homogeneous regions in natural

images, and it is hard for PCNN to position bound-

aries between different regions precisely. On the con-

trary, by minimizing an energy functional, the level

set (LS) method, which was proposed by S. Osher

and J. A. Sethian (Osher and Sethian, 1988), is able

to achieve accurate detection of the edges of homo-

geneous regions on condition that the sketchy posi-

tions of the homogeneous regions are known. There-

fore, we deem that PCNN and LS have complemen-

tary advantages in image segmentation. In our pre-

vious work, W. Xie et al. (Xie et al., 2015) pro-

pose to combine PCNN and traditional LS to achieve

breast mass segmentation. However, traditional LS

methods represent a contour or interface as the zero

level set of a level set function (LSF), and formu-

late the evolution of the contour through the evolu-

tion of the LSF, which typically develops irregulari-

ties during its evolution, may cause numerical errors

and eventually destroy the stability of the evolution

(Li et al., 2010). We therefore use the DRLSE (Li

et al., 2010), which is an improved LS model, instead

of using traditional LS methods. Speciﬁcally, the pro-

posed strategy employs PCNN to ﬁx the sketchy po-

sition of mass by coarsely segmentation. After that,

based on the coarsely segmentation result of PCNN,

some basic geometric methods are used to set initial

contour for DRLSE automatically. At last, the task

of ﬁnely segmentation is carried out by DRLSE. Ex-

perimental results on three mammography databases

tell that the segmentation results are very close to the

ground-truth, which prove the accuracy of the pro-

posed method.

2 RELATED WORKS

The deﬁnitions and principles of PCNN and DRLSE

are successively reviewed and analyzed in the re-

minder of this section.

2.1 Pulse-Coupled Neural Network

As mentioned above, PCNN was proposed to sim-

ulate synchronous pulse bursts in cat visual cortex.

Therefore, compared with traditional artiﬁcial neural

networks, the bio-inspired neuron model of PCNN is

more sophisticated. As illustrated in Figure 1, neu-

ron model of PCNN consists of three parts, i.e., in-

put part, linking part, and pulse generator part (Wang

et al., 2010). The neuron receives information from

input signal and neighborhood neurons through input

part, these two kinds of information are linked by the

linking part, then the pulse generator part generates

pulses as outputs of the neuron.

Threshold

k,l

i,j

Input Part

Linking Part

Pulse Generator

Figure 1: Neuron model of PCNN.

In image processing, pixels of the input image

should be mapped into neurons in PCNN. The neuron

model which is presented in Figure 1 can be quantita-

tively described as iteration by (Eckhorn et al., 1989;

Eckhorn et al., 1990; Wang et al., 2010)

i, j

[n] =e

−α

i, j

[n− 1]

∑

k,l

i, j,k,l

k,l

[n− 1]+ S

i, j

(1)

i, j

[n] =e

−α

i, j

[n− 1]

∑

k,l

i, j,k,l

k,l

[n− 1],

(2)

i, j

[n] = F

i, j

[n](1+ βL

i, j

[n]), (3)

i, j

[n] = e

−α

i, j

[n− 1] +V

i, j

[n], (4)

i, j

[n] =



i, j

[n] > T

i, j

[n]

otherwise

, (5)

where (i, j) is the coordinates of the pixel which is

corresponding to the current neuron. n denotes itera-

tion steps. S

i, j

is the input signal, i.e., gray-level value

BIOIMAGING 2016 - 3rd International Conference on Bioimaging

Original mammography

Neuron map

Neurons of PCNN

fire by iteration

Preprocessing

Setting initial contour for DRLSE

Finely segmentation

using DRLSE

Segmentation result

Step 2: Coarsely segmentation and setting

initial contour for DRLSE

Step 3: Finely segmentation

Binary image

Mapping

Step 1: Preprocessing

Figure 2: A graphical illustration of the proposed method.

of the current pixel. F

i, j

and L

i, j

are feedback input

and linking input of the current neuron. w

i, j,k,l

and

i, j,k,l

are constant synaptic weight matrices for feed-

ing and linking inputs, respectively, which depends on

the distance between the current neuron (i, j) and its

neighbourhood neuron (k, l). F

i, j

and L

i, j

are mod-

ulated through a linking strength β. U

i, j

, which is

named as internal activity, is the modulating result of

i, j

and L

i, j

. T

i, j

is a dynamic threshold which decays

by coefﬁcients α

while the current neuron is not yet

ﬁred. The neuron generates a pulse toY

i, j

while the in-

ternal activity U

i, j

is greater than the dynamic thresh-

old T

i, j

, otherwise, it remains quiet. In other words,

i, j

is the output of the current neuron. V

, V

, and

are initial amplitudes of F

i, j

, L

i, j

, and T

i, j

, respec-

tively. α

and α

are decaying coefﬁcients of F

i, j

and

i, j

, respectively.

2.2 Distance Regularized Level Set

Evolution

As mentioned above, the LS method represents a con-

tour or interface as the zero level set of a LSF, and

formulates the evolution of the contour through the

evolution of the LSF (Estellers et al., 2012). How-

ever, traditional LSF typically develops irregularities

during its evolution, which may cause numerical er-

rors and eventually destroy the stability of the evolu-

tion (Li et al., 2010). To solve this problem, C. Li et

al. (Li et al., 2010) constructed a new type of LS evo-

lution, which ia abbreviated DRLSE, by deﬁning an

energy functional ε(φ) by (Li et al., 2010)

ε(φ) = µR

(φ) + ε

ext

(φ), (6)

where µ is a positive constant, ε

ext

(φ) is an external

energy term, and R

(φ) is the level set regularization

term deﬁned by (Li et al., 2010)

(φ)

∆

Ω

p(|∇φ|)dx, (7)

where p is an energy density function p : [ 0, ∞) → R.

In image segmentation applications, an edge indi-

cator function g can be deﬁned by (Li et al., 2010)

g ,

1+ |∇G

∗ I|

, (8)

where G

is a Gaussian kernel with a standard devi-

ation σ. For a LSF φ : Ω → R, the line integral of

the function g along the zero level contour of φ can be

calculated with the Dirac delta function δ as

(φ) ,

Ω

gδ(φ)|∇φ|dx, (9)

and a weighted area of the region Ω

−

{x : φ(x) < 0} can be calculated with the Heav-

iside function H as (Li et al., 2010)

(φ) ,

Ω

gH (−φ)dx. (10)

Figure 3: Schematic diagram of deﬁning initial contour for

DRLSE.

Automated Breast Mass Segmentation using Pulse-Coupled Neural Network and Distance Regularized Level Set Evolution: A

Coarse-to-ﬁne Approach

Then the energy functional ε(φ), i.e., (6), can be

achieved by (Li et al., 2010)

ε(φ) = µR

(φ) + λL

(φ) + αA

(φ), (11)

where λ and α, which are subject to λ > 0 and α ∈ R ,

are the coefﬁcients of the energy functionals L

(φ)

and A

(φ), respectively. In practice, the Dirac delta

function δ and the Heaviside function H can be ap-

proximated by smooth functions δ

and the Heaviside

function H

, which are deﬁned by (Li et al., 2010)

(x) =



2ε



1+ cos



πx



, |x| ≤ ε

0, |x| > ε

(12)

and

(x) =









sin



πx



, |x| ≤ ε

1, |x| > ε

0, |x| < −ε

, (13)

respectively. By replacing δ and H with (12) and

(13) respectively, we can achieve the energy func-

tional ε(φ) by (Li et al., 2010)

(φ) =µ

Ω

p(|∇φ|)dx+ λ

Ω

gδ(φ)|∇φ|dx

+ α

Ω

gH (−φ)dx.

(14)

Given an initial LSF φ(x, 0) = φ

(x), the approxi-

mated energy functional (14) can be minimized by

solving (Li et al., 2010)

∂φ

∂t

=µdiv(d

(|∇φ|)|∇φ|)

+ λδ

(φ)div



∇φ

|∇φ|



+ αgδ

(φ).

(15)

3 THE PROPOSED BREAST

MASS SEGMENTATION

METHOD

The framework of the proposed method, which con-

sists of three steps, is graphically illustrated in Fig-

ure 2. The ﬁrst step is pre-processing, which re-

moves tags, camera windows, and pectoral muscles

from the original mammography. The second step

conducts coarsely segmentation and sets initial con-

tour for DRLSE. The last step achieves ﬁnely seg-

mentation by using DRLSE. From another angle, the

procedure of coarsely segmentation can be regarded

as extracting the region of interest (ROI) from mam-

mography. Since our method ﬁrstly extracts ROI with

PCNN and then performs ﬁnely segmentation with

DRLSE, we deﬁne it as a ‘coarse-to-ﬁne’ strategy.

(a) (b)

Figure 4: Visualization of the LSFs before and after evolu-

tion. (a) Initial contour. (b) Initial LSF. (c) Final contour.

(d) Final LSF.

3.1 Coarsely Segmentation with PCNN

In the ﬁeld of CAD, a ROI usually denotes a region

which provides the rough location of a lesion and ex-

cludes normal tissues as much as possible (Xian et al.,

2015). In this work, the ROI of each mammography

is extracted by using PCNN to conduct coarsely seg-

mentation. As illustrated in Figure 2, each pixel of the

digitized mammography is corresponded to a neuron

in the PCNN, and the gray-level values of the pix-

els are regarded as the inputs of the corresponding

neurons, i.e., S

i, j

= G

i, j

, where G

i, j

denotes the gray-

level value of the pixel (i, j) in original mammogra-

phy. Then we initialize the PCNN by setting initial

values to its parameters. This work uses an adaptive

method, which was proposed in our previous work

(Chen et al., 2011), to initialize the parameters for

PCNN. After initialization, the neurons of PCNN are

able to ﬁre by iteration following (1)-(5). As a basic

strength, PCNN is able to cause the adjacent neurons

with similar inputs to pulse synchronously, resulting

in great potential in image segmentation. Since we

have corresponded pixels to PCNN neurons without

changing their original locations, adjacent pixels with

similar gray-level can be easily partitioned from the

whole image by the procedure of synchronously ﬁr-

ing. At the last iteration of PCNN, we should obtain a

binary image. Each pixel of the binary image records

whether its corresponding neuron has ﬁred. The bi-

nary image is the result of the coarsely segmentation,

i.e., ROI.

BIOIMAGING 2016 - 3rd International Conference on Bioimaging

3.2 Deﬁning Initial Contour for DRLSE

In this step, we use some simple yet effective geomet-

ric methods to deﬁne initial contour for DRLSE. As

illustrated in Figure 3, the largest rectangle which is

tangent to the foreground of the binary image is de-

ﬁned as the initial contour for DRLSE. Speciﬁcally,

the foreground of the binary image denotes the largest

close region in the image.

3.3 Finely Segmentation with DRLSE

Based on the results of preprocessing and coarsely

segmentation (i.e., extraction of ROI), ﬁnely segmen-

tation is achieved by DRLSE. To help the readers un-

derstand the process of evolution, Figure 4 visualizes

the LSFs before and after evolution. Let’s denote by

i, j

the LSF with spatial index (i, j) and temporal in-

dex k. The LS evolution equation can be formulated

by an iteration process as (Li et al., 2010)

k+1

i, j

= φ

i, j

+ ∆tL



i, j



, k = 0, 1, 2, ··· , (16)

where L



i, j



denotes the approximation of the right

hand side in the evolution equations, and is deﬁned by



i, j



k+1

i, j

− φ

i, j

∆t

. (17)

In our implementation, the time-step ∆t is ﬁxed

as ∆t = 5, and the parameters in the DRLSE model,

i.e., equation (15), are set as µ = 0.04 and λ = 5, re-

spectively. The value of α can be ﬂexibly adjusted

according to the contrast of the mammography.

4 EXPERIMENTAL RESULTS

4.1 Database and Settings

To provide a full spectrum of possible cases, the

mammographies used in this work are taken from the

Mammographic Image Analysis Society (MIAS) dig-

ital mammography database (Suckling et al., 1994),

the digital database for screening mammography

(DDSM) (Heath et al., 1998; Heath et al., 2001), and

the mammography database of the Japanese Society

of Medical Imaging Technology (JAMIT). The infor-

mation of the three databases are summarized in Table

As can be seen from Table 1, the patients of the

three databases are from three different continents:

Europe, America, and Asia, respectively. The MIAS

Table 1: Overview of the mammography databases.

Database Provider Continent

MIAS Pilot European Image Processing Archive Europe

DDSM

Department of Computer Science and

Engineering, University of South Florida

America

JAMIT

Japanese Society of Medical Imaging

Technology

Asia

Figure 5: Typical segmentation results on MIAS database

(corresponding patients are from Europe). In each column,

from top to bottom: Original mammography, coarsely seg-

mentation result from PCNN, and ﬁnely segmentation result

from DRLSE.

digital mammography database contains 322 mam-

mography screening exams which have been digi-

tized to 50 micron per pixel with a linear optical den-

sity in the range 0-3.2, and each pixel is represented

with an 8-bit word. The resolution of the images

in MIAS database is 1024×1024 pixels. The im-

ages were annotated according to their correspond-

ing types of abnormality by expert radiologists, us-

ing seven distinct classes: Calciﬁcation (23 images),

well-deﬁned/circumscribed mass (23 images), specu-

lated mass (19 images), ill-deﬁned mass (14 images),

architectural distortion (19 images), asymmetry (15

images), and normal (209 images). The DDSM

database contains 2620, four view, mammography

screening exams. The mammography screening ex-

ams in DDSM database were digitized by one of four

different scanners: DBA M2100 ImageClear (42 mi-

cron per pixel, 16 bit depth), HOWTEK 960 (43.5

micron per pixel, 12 bit depth), Lumisys 200 laser

densitometer (50 micron per pixel, 12 bit depth), and

HOWTEK MultiRAD 850 (43.5 micron per pixel, 12

bit depth) (Chen et al., 2015). The size of the images

Automated Breast Mass Segmentation using Pulse-Coupled Neural Network and Distance Regularized Level Set Evolution: A

Coarse-to-ﬁne Approach

in DDSM database varies, and the pathology types

were annotated by expert radiologists as normal, can-

cer, benign, and benign without callback. The JAMIT

mammography database contains 40 mammography

screening exams which have been classiﬁed into three

categories: normal (17 images), calciﬁcation (11 im-

ages), and tumor (12 images). Each of the mammog-

raphy screening exams in the JAMIT mammography

database was digitized to 2510×2000 pixels, and the

bit-depth of each pixel is 10 (Arai et al., 2012). In this

work, since the proposed approach focus on breast

mass segmentation, only mammographies that con-

tain a mass might be used for experiments. To bring

the mammographies in DDSM and JAMIT databases

into correspondence with those in MIAS database,

they are resized to 1024×1024 pixels before segmen-

tation. Following experiments are implemented by

programming with the Mathworks Matlab 2010b on

a PC with Intel Core-i5 processer and 4 GB RAM.

4.2 Experimental Results

Both subjective and objective evaluations on the per-

formance of the proposed method are presented in this

subsection. For European patients, Figure 5 presents

several typical segmentation results of the proposed

method on MIAS digital mammographydatabase. By

comparing the ﬁrst row and the second row of Figure

5, we can see that the masses in the original mam-

mographies are accurately positioned, and the rough

shapes of the masses are also accurately contoured by

PCNN. Besides, by examining the last row of Figure

5, we can ﬁnd that the boundaries between masses

and backgrounds are precisely positioned by DRLSE.

For American and Asian patients, Figure 6 and Figure

7 present the typical segmentation results of the pro-

posed method on DDSM and JAMIT mammography

databases, respectively. The same conclusion can be

drawn from Figure 6 and Figure 7. This is due to that

the initial contour of DRLSE is adaptively deﬁned in

a mammography-speciﬁcway and DRLSE can ensure

stable LS evolution.

The quantitative segmentation results of the pro-

posed method and the ground-truth are listed in Table

2. In MIAS database, four mammographies, which

contain circumscribed mass, asymmetry, ill-deﬁned

mass, and spiculated mass, respectively, were tested.

In DDSM database, four mammographies, which

contain masses with irregular shape, lobulated shape,

and oval shape, were tested. In JAMIT database,

four mammographies which contain tumors were also

tested. The coarse and ﬁne regions in Table 2 de-

note the coarsely and ﬁnely segmentation results of

the proposed method, respectively. For each mam-

Figure 6: Typical segmentation results on DDSM database

(corresponding patients are from America). In each col-

umn, from top to bottom: Original mammography, coarsely

segmentation result from PCNN, ﬁnely segmentation re-

sult from DRLSE, and expert diagnosis (Heath et al., 1998;

Heath et al., 2001).

mography, the MIAS mammography database pro-

vides us the coordinates of the centre of the abnor-

mality and the approximate radius of a circle enclos-

ing the abnormality. So the ground-truth regions of

abnormalities in MIAS mammography database can

be calculated with the coordinates and the radius.

For DDSM mammography database, only thumbnail

images which contain suspicious regions are avail-

able. So the ground-truth regions of abnormalities

in DDSM mammography database can be approxi-

mately positioned with the thumbnail images. The

fourth column of Table 2 lists the ground-truth, and

the last two columns list the experimental results of

the proposed algorithm. Comparing our results with

ground-truth, we can ﬁnd that, for every types of ab-

BIOIMAGING 2016 - 3rd International Conference on Bioimaging

Table 2: Performance of the proposed method.

Database Mammography Abnormality Ground-Truth Region Coarse Region Fine Region

MIAS

mdb021 circumscribed mass

(444:542, 850:948) (480:525, 880:915) (428:580, 844:943)

mdb081 asymmetry (361:623, 420:682) (420:560, 500:625) (352:587, 426:635)

mdb134 ill-defined mass (420:518, 247:345) (450:490, 273:328) (433:516, 247:341)

mdb202 spiculated mass (520:594, 215:289) (535:580, 235:275) (510:596, 240:290)

DDSM

A_1004_1.RIGHT_CC mass: irregular shape (254:503, 399:566) (278:495, 411:559) (255: 492, 399:567)

A_1006_1.LEFT_MLO mass: irregular shape (577:916, 566:713) (657:901, 580:715) (681:891, 575:705)

C_0011_1.RIGHT_CC mass: lobulated shape (661:977, 565:745) (711:878, 618:706) (703:912, 567:719)

C_0088_1.LEFT_CC mass: oval shape (128:515, 401:631) (156:461, 438:592) (199:432, 438:591)

JAMIT

T1 tumor / (517:676, 740:939) (535:636, 711:845)

T3 tumor / (412:490, 874:956) (414:492, 882:966)

T5 tumor / (528:596, 857:950) (531:596, 868:935)

T7 tumor /

(508:609, 206:326)

(429:565, 865:794)

(507:616, 185:328)

(442:578, 629:783)

Noting that the positions of the mass regions were fixed after resizing.

Figure 7: Typical segmentation results on JAMIT database

(corresponding patients are from Asia). In each column,

from top to bottom: Original mammography, coarsely seg-

mentation result from PCNN, ﬁnely segmentation result

from DRLSE, and expert diagnosis.

normalities, almost all of our results are inside the

corresponding regions of ground-truth. This suggests

that the proposed method encloses little normal tis-

sues, proving the accuracy of our method.

5 CONCLUSION

We have presented a coarse-to-ﬁne method for auto-

matic breast mass segmentation in this paper. Specif-

ically, PCNN is employed to ﬁx the sketchy position

of mass and extract ROI in the ﬁrst step. The initial

contour of DRLSE is then adaptively deﬁned accord-

ing to the position and the shape of the ROI. Finally,

the precise boundary between mass and background

is positioned with DRLSE. This is a novel and effec-

tive approach to partition mass in mammography for

following reasons. First, ROI is extracted by PCNN

in advance, which brings about a ﬂexible and auto-

matic deﬁnition of initial contour for DRLSE. Sec-

ond, the DRLSE, which can ensure accurate compu-

tation and stable LS evolution, is ﬁrstly employed for

the purpose of breast mass segmentation. Both sub-

jective and objective experiments on three large-scale

mammography databases prove that the ROI can be

accurately ﬁxed by PCNN, and the boundary between

mass and normal tissue can be precisely positioned

by DRLSE. In summary, the proposed method shows

expectant segmentation performance.

On the other hand, considering its efﬁciency and

accuracy, we expect that, in the future, the proposed

breast mass segmentation algorithm will ﬁnd its util-

ity in more applications of medical image segmenta-

tion, such as vessel segmentation in fundus images

and heart chamber segmentation in computed tomog-

raphy (CT) images.

ACKNOWLEDGEMENTS

The authors would like to thank Dr. Chunming Li for

shearing the source code of distance regularized level

set evolution, and the members of the Institute of Cir-

cuits and Systems, School of Information Science and

Engineering, Lanzhou University, for fruitful discus-

sions and valuable advices. This work was supported

by the National Natural Science Foundation of China

(NSFC) under Grant 61175012, the Specialized Re-

Automated Breast Mass Segmentation using Pulse-Coupled Neural Network and Distance Regularized Level Set Evolution: A

Coarse-to-ﬁne Approach

search Fund for the Doctoral Program of Higher Ed-

ucation of China under Grant 20110211110026, and

the Fundamental Research Funds for the Central Uni-

versities of China under Grant lzujbky-2015-196.

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BIOIMAGING 2016 - 3rd International Conference on Bioimaging