Breast Mass Segmentation Using a Semi-automatic Procedure Based on Fuzzy C-means Clustering

TELKOMNIKA, Vol.16, No.2, April 2018, pp. 665~672
ISSN: 1693-6930, accredited A by DIKTI, Decree No: 58/DIKTI/Kep/2013
DOI: 10.12928/TELKOMNIKA.v16i2.6193  665
Received April 6, 2017; Revised October 16, 2017; Accepted February 1, 2018
Breast Mass Segmentation Using a Semi-automatic
Procedure Based on Fuzzy C-means Clustering
Moustapha Mohamed Saleck*
1
, Abdelmajid El Moutaouakkil
2
, Mohammed Moucouf
3
, Maksi
Bouchaib
4
, Hani Samira
5
, Jamaldine Zineb
6
1,2
Dept. of Computer Science, Chouaib Doukkali University, El Jadida-Morocco
3
Dept. of Mathematics, Chouaib Doukkali University, El Jadida-Morocco
4,5,6
Dept. of Radiology, Hospital Mohamed V, El Jadida-Morocco
*Corresponding author, e-mail: saleck.moustapha@gmail.com
Abstract
Mammography is the primary modality that helped in the early detection and diagnosis of women
breast diseases. Further, the process of extracting the masses in mammogram represents a challenging task
facing the radiologists, due to problems such as fuzzy or speculated borders, low contrast and the presence of
intensity inhomogeneities. Aims to help the radiologists in the diagnosis of breast cancer, many approaches
have been conducted to automatically segment the masses in mammograms. Towards this aim, in this paper,
we present a new approach for extraction of tumors from region-of-interest (ROI) using the algorithm of Fuzzy
C-Means (FCM) setting two clusters for semi-automated segmentation. The proposed method meant to select
as input data the set of pixels that enable to get the meaningful information required to segment the masses
with high accuracy.This could be accomplished through eliminating unnecessary pixels, which influence on this
process through separating it outside of the input data using an optimal threshold given by monitoring the
change of clusters rate during the process of threshold decrementing. The proposed methodology has
successfully segmented the masses, with an average sensitivity of 82.02% and specificity of 98.23%.
Keywords:breastcancer detection; fuzzy c-means clustering;threshold;median filtering;segmentation
Copyright © 2018 Universitas Ahmad Dahlan. All rights reserved.
1. Introduction
According to American cancer society, breast cancer is the most commonly diagnosed
cancer type among women [1]. If the physicians are capable to detect cancer in its primary stages
this would give women the maximum opportunity to stay alive, because the early check and
detection of cancer is the most important thing in curing breast cancer. Radiologists and doctors
could use computer aided diagnosis (CAD) system to help them making a reliable diagnosis of the
breast cancer in its earlier stages [2]. Breast segmentation is a major step in various CAD methods;
its performance and efficiency directly affect the performance of the subsequent processing steps in
mammogram analysis.
Many approaches have been proposed to segment masses from surrounding tissues in
digital mammograms. Mahfuzah Mustafa and al [3], used Chan-Vese Active Contour and Localized
Active Contour for segmenting lesions in digitized mammogram images, the effectiveness of these
techniques are then compared, the results obtained by Chan-Vese Active Contour are proven to be
better than the Localized Active Contour method. J. Quintanilla et al [4], proposed mathematical
morphology to enhance potential MCs. Afterwards, three algorithms (Fuzzy C-Means, K-Means, and
Possibilistic Fuzzy c-Means) are used and compared in order to segment ROIs images, trying to
improve the results of microcalcifications cluster detection.
J. Anitha et al [5], proposed a new computer aided approach to detect the suspicious lesions by
using a Dual Stage Adaptive Thresholding (DuSAT), the authors used global histogram and local
window thresholding method to detect the suspicious mass region. Q.Abbas et al [6], presented a
new method for automatic breast mass segmentation in mammograms by combining the region-
based and the edge-based techniques in four stages, using dynamic contrast improvement to select
ROI, detection of mass candidate points by prior and posterior probabilities based on robust
multiscale feature-fusion, and final delineation of the mass region by a MAP scheme.
D. Pereira et al [7], implemented an artifact removal algorithm in CC and MLO views, the wavelet
transform and Wiener filter are used for image enhancement, finally, the authors employed multiple

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TELKOMNIKA Vol. 16, No. 2, April 2018 : 665 – 672
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thresholding wavelet transform and genetic algorithm for masses detection and segmentation.
J. Anitha et al [8], proposed spatial based fuzzy level set algorithm for automatic mammogram mass
segmentation, the output of fuzzy clustering is used as input for the level set segmentation that is
used to refine the mass boundary, the results of fuzzy clustering are used to estimate the control
parameters of level set algorithm. Khaddouj Taifi et al [9], proposed a hybrid technique for
enhancing mammograms, this technique combines Nonsubsampled Contourlet Transform and
Hommomorphic Filtering, then the authors presented a comparative study based on three different
algorithms: proposed methods, homomorphic filtering and unsharp masking, the experimental results
of this study show that the proposed method significantly reduces noise in high noise mammograms.
The choice of the cluster number is one of the biggest challenges to ensure better
segmentation result by Fuzzy C-means; because the performance of segmentation result strongly
depends on the number of attached clusters.
Original C=2 C=3
C=4 C=5 C=6
Figure 1. The Different Clustering Results
Despite the large number of studies that have been developed in order to get the optimal
number of clusters, and to adapt the fuzzy c-means for automatic image segmentation [10-11], an
automatic processing for tumor segmentation by using FCM is still challenging in CAD system.
Therefore, the aim of this paper is to overcome this limitation; and to present a novel breast mass
segmentation method based on Fuzzy c-means algorithm, where the number of clusters is C=2.
Aiming to get one of these clusters represent the mass with high accuracy, we introduce a threshold
that allows selecting as input data just the meaningful set of pixels. After doing the noise reduction by
using median filter, the process of decrementing starts with increasing the number of input data and
account the amount of output data in each cluster. the change of output data amount, during
threshold decrementing subject to examination in order to obtain an optimal threshold, which will
enable us to select as FCM input, just the set of pixels suitable and enough to be used to doing the
best segmentation.
The paper is organized as follows: Section 2 describes the materials and proposed
methodology for segmentation of mammographic masses. Section 3 demonstrates the results and
performance. Finally, section 4 presents the conclusion.
2. Research Method
In this part, we present details of the various methods and materials used in this study.
2.1. Database Description
Mammography Image Analysis Society (MIAS) has created a mammogram database. In this
work, we use this database. The original MIAS database is digitized at 50-micron pixel edge and has
been reduced to 200-micron pixel edge and clipped or padded so that every image is 1024 X 1024
pixels. All images are held as 8-bit gray level scale images with 256 different gray levels (0-255)
and physically in portable gray map (PGM) format.

TELKOMNIKA ISSN: 1693-6930 
Breast Mass Segmentation Using a Semi-automatic Procedure… (Moustapha Mohamed Saleck)
667
2.2. Preprocessing
Image preprocessing is necessary to improve the quality of the image and to remove the
unwanted noise from mammogram images, which allows improving the accuracy of segmentation
results. For this reason, 2D-median filter [11], is applied in this work. The method is called semi-
automatic because of the need of selection of ROI by an expert.
We have taken a square region of area 174x174 pixels as the ROI for this study, the value
174 is chosen in consultation with the radiologist because it is the radius of the largest mass present
in the database [12].
Mdb23
Mdb5
Figure 2. ROI (174x174) Obtained from mdb5 and mdb23
2.3. Fuzzy C-Means Clustering
Fuzzy C-Means Clustering (FCM) algorithm was first introduced by Dunn [13] in 1973 and
improved by Bezdek [14] in 1981. This algorithm is one of the most commonly used unsupervised
pattern recognition approaches for tumor segmentation [15]. In the Fuzzy c-means, each image pixel
has certain membership degree associated with each cluster centroid. These membership degrees
have values between 0 and 1. FCM based on optimizing the following objective function (1):
∑ ∑ ( ) (1)
( ( ))
∑ ( ( ))
(2)
∑
∑
(3)
Where m >1 is the fuzzy index, n is the total number of pixels in image, is the membership degree
of k the pixel to the centroid . ‖ ‖ is the distance between and The function
J (1) is optimized with an iterative function to achieve fuzzy partitioning and the elements of the
membership matrix and the cluster center is updated and is given by (2) and (3).
2.4. Basic Idea
As previously mentioned, the estimation of the optimal number of clusters often affects the
performance and efficiency of the Fuzzy c-means algorithm in image segmentation. For this reason,
the proposed methodology aims to provide a solution to overcome the sensitivity of the Fuzzy c -
means to the clusters number in mammogram images segmentation by finding a range of
appropriate pixels (The input FCM), where application of fuzzy c-means algorithm within this range
can produce two clusters only as output, one of them represents the breast tumor. The first limit of
the appropriate range is defined by the maximum gray level (MG) because the abnormal lesions in
mammography images characterized by high-intensity values compared to other regions of the

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breast. While, an optimal threshold (Opt), represents the second limit of this range.
To find an optimal threshold (Opt), we have used a decrement operator (P) and a threshold
testing (Tt) initialized by Tt=MG – P. For each iteration the set of pixels existing inside of the range
[Tt-MG], should be used as input of FCM, then it subject to clustering, after that, we compute the
percentage of output data in each cluster (cluster_1, cluster_2), and the set of pixels unclassified
(outside of the range). The basic idea of decrement threshold is to increase the amount of pixels
(input data) which subject to clustering, thus finding different results of output clusters depending on
the input data, from these results we can accurately determine an appropriate threshold.
Figure 3. Process of Clustering During Threshold Decrementing
3. Results and Analysis
In this section, it is explained the results of research and at the same time is given the
comprehensive discussion. Results can be presented in figures, graphs, tables and others that make
the reader understand easily [2], [5]. The discussion can be made in several sub-chapters.
3.1. Sub Bab 1
xx
3.2. Sub Bab 2
yy
4. Conclusion
Figure 4. Examples of Results from Different Range of Application. (a) Mdb23, (b) Mdb5, (c) Mdb15.
.
.
.
.
.
.
.
.
[MG……..Tt…. ]
FCM
Cluster_1
( %)
Cluster_2
( %)
Unclassified ( x%)
Cluster_1
( %)
Cluster_2
( %)
Unclassified = 0%
FCM
[MG…………. ]
.
.
.
.
.
.
.
.
.
.
.
.
= – P, where P=5 ;
𝐉 ∑ ∑ 𝐔𝐢𝐤 𝐝 ( 𝐱 𝐤 𝐯𝐢)𝐧
𝐤𝐢 , Where 𝒙 𝒌 ∈ [Opt – MG] (4)
(a) MG = 201 [196-201] [186-201] [176-201] [166-201] [156-201] [146-201] [141-201 ] [46-201]
(b) MG = 186 [176-186] [156-186 ] [146-186] [136-186 ] [126-186 ] [106-186] [101-186 ] [28-186]
(c) MG =208 [203-208] [193-208] [183-208] [173-208] [163-208] [138-208] [108-208] [118-208]

669
(a)
(b)
(c)
Figure 5. Rate of Cluster_1, Cluster_2, Unclassified Pixels, During Process of Threshold Decrementing.
(a) Mdb23, (b) Mdb5, (c) Mdb15.
In observing the evolution of output data from cluster_1 and cluster_2 during the process of
threshold test decrementing, we can distinguish two kinds of results. The first case is illustrated in
figure 5. (a) and (b) shows an increase and strong growth at the cluster_1 level, followed by a
sudden decrease. In contrast, the cluster_2 that represent the masses, begin to grow by a low
increase followed by a sudden sharp increase. The sudden change in the evolution of the cluster
happens at the same point of threshold test (crucial point), this point represents an optimal threshold
of segmentation. So, we can use the following formula to find an appropriate threshold.
= , = . // , are cluster_1 and cluster_2.
// are tw o successive points fromthe curve of
clusters evolution in process of decrementing.
// optimal threshold.
// : are the gradients of , .
The second case is illustrated in figure 5.(c), it can be noted that there is an increase
homogeneous followed by fixed evolution at the cluster_1 level and a similar increase with a stronger
rate after a few iterations at the cluster_2 level, followed by fixed evolution. In contrast with the
previous situation, in this case, all pixels are used as input data to segment the mass from ROI. So,
this situation is achieved when:
, Where // : minimum gray level.
𝑓 𝑆 𝑇𝑡
< 𝑂𝑝𝑡 𝑇𝑡
Where 𝑆 𝑇𝑡
𝜕 𝑐 𝜕𝑐

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670
Choosing a high value of decrement operator (P) gives divergent thresholds for testing
whereas many values have been exceeded, this divergence influences on the accuracy of threshold
optimal. On the other hand, when the parameter takes a low value, so we have a long time to
perform the process of segmentation, for this reason, and after many tests with several images in the
database MIAS, we obtained good results with P= 5.The morphological operation “area open”
applied, to retain only the biggest region which represents the mass and removes all other regions.
Figure 6. The Output Images
3. Experiment Results
In this work, the proposed method is tested by using a set of 21 digitized mammograms
contain 22 masses taken from MIAS database. Our system was implemented in Java 8.0 using the
Java Advanced Imaging (JAI) API. The process of extracting the masses from ROIs achieved without
user intervention.
To evaluate the method all masses contours are manually marked by one of three
radiologists (one ground truth agreed by three radiologists in the same examination session) from El
Jadida hospital, in Morocco, based on the visual criteria.
Figure 7. True Positives, True negatives, False Positives, and False Negatives definition.
Where the white contours lines represent the contours denoted by a radiologist, and the
black contours lines are the segmentation results using the proposed method.
TP represents the intersection between the segmentation results given by the radiologist and the
segmentation results given by the system, FN is the segmentation obtained by the radiologist, FP
represents the segmentation obtained by the system and TN is the region outside the segmentation
obtained by both the radiologist and the system. In order to evaluate the performance of
segmentation, the following measures are used.
Sensitivity= (5)
Specificity= (6)
Accuracy= (7)
Pixels
Unclassified
[146 - 201] [106 - 186] [118 - 208]Optimal ranges
Cluster_2
Cluster_1
(Mass cluster)

671
Overlap= (8)
The overlap indicates how much the mass boundary delineated by an algorithm intersects
with the mass boundary delineated by radiologists. The sensitivity measure indicates the probability
that pixels are classified among the diseases (TP).The specificity measure indicates the probability
that the pixels are classified among the non-disease (TN). Accuracy indicates the ratio of correctly
classified pixels to the ROI.
Table 1. The Performance Criterion Applied to Images from MIAS Database
Name Overlap Sensitivity Specificity Accuracy
Mdb 005 67.39 80.13 95.90 93.09
Mdb 010 89.76 94.36 99.53 99.10
Mdb 012 62.94 66.77 99.21 95.52
Mdb 015 90.44 91.24 99.67 97.40
Mdb 021 63.42 69.47 97.79 92.48
Mdb 023 83.74 93.57 98.85 98.38
Mdb 025 79.75 91.23 93.01 92.43
Mdb 028 82.67 94.17 94.11 94.13
Mdb 069 47.80 52.84 97.49 88.92
Mdb 092 63.31 67.97 99.04 95.45
Mdb 095 69.11 69.93 99.87 97.12
Mdb 097 51.34 51.87 99.88 94.88
Mdb 102 81.59 85.90 98.62 96.01
Mdb 132 60.47 98.93 95.34 95.59
Mdb 132 77.43 77.64 99.99 99.44
Mdb 142 74.66 85.47 99.02 98.16
Mdb 144 84.22 86.66 99.87 99.31
Mdb 145 71.01 79.42 99.47 98.62
Mdb 202 80.60 90.67 98.25 97.32
Mdb 267 84.13 96.58 97.08 97.09
Mdb 271 86.39 87.02 99.64 95.41
Mdb 312 80.16 92.70 99.43 99.20
Average 82.02 98.23 74.20 96.28
We give in the Table the results of some existing algorithms to compare the performance of
the proposed method with previously explored methods.
Table 2. Results Comparison
Paper
Ref. No
Proposed Method
Results
DatabaseOverlap Sensitivity Specificity Accuracy
[3] Chan-Vese Active Contour - - - - - - 90 % DDSM
[7] w avelet analysis, genetic algorithm 79.2% - - - - - - DDSM
[5]
dual stage adaptive thresholding
(DuSAT)
- - 93.5% - - - -
- - 92.5% - - - -
MIAS
DDSM
[16]
Mean Shift Segmentation and Iris
Filter
60 % 81% - - - -
Shiga
University
4. Conclusion
It is often difficult to extract a tumor from normal breast tissues by radiologists in
mammogram images, because of their ambiguous margins and subtle appearance. In this paper, we
have proposed a semi-automatic segmentation using FCM where the number of clusters is known
without estimation. The input of FCM is a set of pixels taken from a range limited by maximum gray
level and a threshold; these pixels represent the set of data which will allow us to get two clusters
only, one of them represents the tumor with high accuracy. The results obtained demonstrate the
efficiency and effectiveness of our proposed method.

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Breast Mass Segmentation Using a Semi-automatic Procedure Based on Fuzzy C-means Clustering

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