The Potential Use of Bioinspired Algorithms Applied in the
Segmentation of Mammograms
David González-Patiño
, Yenny Villuendas-Rey
and Amadeo J. Argüelles-Cruz
Centro de Investigación en Computación del Instituto Politécnico Nacional, Avenida Juan de Dios Bátiz esq. Miguel
Othón de Mendizábal, Nueva Industrial Vallejo, Gustavo A. Madero, 07738, Ciudad de México, D.F., Mexico
Centro de Innovación y Desarrollo Tecnológico en Cómputo del Instituto Politécnico Nacional, Av. Juan de Dios Bátiz s/n,
Nueva Industrial Vallejo, Gustavo A. Madero, 07700 Ciudad de México, D.F., Mexico
Keywords: Segmentation, Mammography, Bioinspired Algorithm.
Abstract: In this article, we present the potential use of bioinspired algorithms for segmentation. The comparison is
done with 3 bioinspired algorithms and Otsu method, which is an algorithm currently used to perform image
segmentation. The vast majority of bioinspired algorithms were designed for optimization, however in this
work, an adjustment is done to use the function to be optimized as a function that allows us to segment an
image. The results in this work showed that the bioinspired algorithms are a good alternative to perform the
task of segmentation in medical images, specifically mammography.
Breast cancer is a global problem and one of the
cancers that has caused more than 410,000 deaths
until 2002 (Ferlay et al., 2010). An early diagnosis
of breast cancer will result in more opportunities and
treatments which will reduce death risk of the
This is the reason that there are mammography
segmentation techniques, which aim to detect small
abnormal regions within the breast. This
segmentation process allows us to detect regions of
interest in the medical image and, in case of being
necessary, to carry out a subsequent treatment by the
medical specialist.
In this work we explore the possibility of using
bioinspired algorithms for the segmentation of
mammographies. These algorithms are based on a
biological process used to find an optimal solution to
a problem.
Although many of the bio-inspired algorithms
are mainly designed to solve optimization problems,
in this paper we propose to use a function that
allows us to transform the optimization process into
a segmentation process.
Nowadays, the use of segmentation algorithms in
medical areas has allowed the early diagnosis of
many diseases including breast cancer.
This paper is organized as follows, In section 2,
some of the previous works are presented. In section
3, the tested algorithms for segmentation are
explained as well as the materials used. The results
and comparison of the segmentation algorithms are
presented in section 4. And finally in section 5, we
present the conclusions and future work related to
this area.
In the existing field called Evolutionary
Computation, there has been many approaches such
as evolutionary methods and swarm intelligence
which have showed good performances solving
problems in computer vision (Vite-Silva et al.,
2007), clustering (Abraham et al., 2008), network
routing (Kassabalidis et al., 2001), among others.
Related to swarm intelligence, some algorithms
have been used in optimization of classifiers
(Ramirez et al., 2015), instance selection (Derrac et
al., 2012) and optimal parameter values selection
(Friedrichs and Igel, 2005).
Catarious et al. proposed a system to identify
suspicious masses in mammograms (Catarious et al.,
2004), similarly in 2006, Scharcanski and Jung
(2006) proposed a method to improve the contrast
and eliminate noise in mammographic images.
Related to bioinspired algorithms, Brodić and
González-Patiño, D., Villuendas-Rey, Y. and Argüelles-Cruz, A.
The Potential Use of Bioinspired Algorithms Applied in the Segmentation of Mammograms.
DOI: 10.5220/0006951103030306
In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 1: KDIR, pages 303-306
ISBN: 978-989-758-330-8
Copyright © 2018 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Milivojević (2011) proposed a modified version of
the water flow algorithm which they applied to the
segmentation of texts.
Other approaches (Bevilacqua et al., 2009) have
been used to detect blood vessels in retinal fundus
using artificial neural networks.
Other works presented by Frausto-Solís et al.,
(2013)in 2013 and, Rad and Lucas (Rad and Lucas,
2007) in 2007 showed the use of bioinspired
algorithms (simulated annealing algorithm and IWO
algorithm respectively) showing good performances.
Although there are many algorithms to perform
the segmentation, there is not yet a perfect algorithm
that allows its use in all segmentation problems, that
is why this work presents modified bioinspired
algorithms that allow us to perform this task.
Many of the bioinspired algorithms were mainly
designed for optimization tasks. However, in this
work, we propose the possibility of using these
algorithms for segmentation by using the Dunn
index and using each individual component as if it
were a gray level of the gray scale. In this work we
made a comparison of the Otsu method (Otsu, 1975)
against 3 bioinspired algorithms (Novel Bat
Algorithm (Meng et al., 2015), Invasive Weed
Optimization (Mehrabian and Lucas, 2006) and
Particle Swarm Optimization (Kennedy and
Eberhart, 1995)).
Given that the bioinspired algorithms used in this
work have as their main component Individuals, it is
necessary to present the representation that they will
have in the system. The composition of an individual
will be given by a vector composed of values that
represent gray levels as presented in figure 1.
Figure 1: Representation of an individual.
3.1 Novel Bat Algorithm (NBA)
Bat algorithm was proposed in 2010 by Yang (Yang,
2010), as an optimization algorithm based on the
behavior of bats and, specifically, the techniques
they use to identify food using echolocation.
Later, Meng (Meng et al., 2015) propose a
modification of the algorithm proposed in 2010,
which he called Novel Bat Algorithm (NBA), this
algorithm includes among other novelties, the
Doppler Effect and the ability to search the global
solution in different habitats. The implementation of
this latest version was the one used for this work.
3.2 Invasive Weed Optimization (IWO)
This algorithm of numeric optimization was
introduced by Mehrabian and Lucas (2006) in 2006
and is based on weed colonies and how they adapt to
the environment to spread their seeds. Unlike other
metaheuristics, this algorithm allows all individuals
(plants) to participate in later generations. This
aspect gives the opportunity to plants with better
performance to produce more seeds and plants with
lower performance, will produce fewer seeds.
Another notable difference of this algorithm is that
individuals produce seeds autonomously, that is, the
mating process does not occur.
3.3 Particle Swarm Optimization
This algorithm proposed by Kennedy and Eberhart
(1995), is based on the movement of the particles.
The search and refinement of candidate solutions is
based on a social swarm model (such as birds or
fishes) that pretends to find food.
3.4 Error Measures
According to Zhang et al., (2008), it is possible to
determine the quality of a segmentation by
calculating two error measures (F and F’). F
measures the average squared color error while F’ is
a modification of F that penalizes segmentations
with small regions.
3.5 Dunn Index
Since the bioinspired algorithms used in this work
were designed for optimization, in this work, the
possibility of using them for segmentation is
proposed changing the function to be optimized as
the division of the minimum outercluster distance
between the maximum intercluster distance. This
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KDIR 2018 - 10th International Conference on Knowledge Discovery and Information Retrieval
relationship is called the Dunn index (Dunn, 1973)
and is used as a function to be maximized since it is
intended to have the greatest distance between
clusters and the smallest distance between the
elements on the same cluster.
In this work, 362 images of real patients were
segmented, which belong to the Breast Cancer
Digital Repository database (Moura and López,
2013). This dataset contains the segmentation of said
images, carried out by a group of radiological
Figure 2: (a) Original image, (b) Overlap of the ROI
segmented by expert radiologist over original image, (c)
Image segmented by Otsu method, (d) Image segmented
by NBA, (e) Image segmented by IWO, (f) Image
segmented by PSO.
In Figure 2, we present a mammographic image
taken at random from the 362 images of the dataset.
This image was segmented by the 4 segmentation
algorithms and the image results are presented in the
The values presented in Fig. 3, are expressed in
for F mean, and 10
for F’ mean. These
values were calculated for the 362 images and the
average is presented in Fig. 3 for each algorithm.
In Figure 3, it is observed that IWO obtained a
lower error than the other bioinspired algorithms.
The other bioinspired algorithms obtained lower
errors than Otsu method, which at the present time
remains as an algorithm widely used to segment.
Figure 3: Mean of the errors calculated for the
segmentation of the 362 mammographies.
Table 1: Mean time in seconds per image segmented.
Algorithm Mean Time per image (seconds)
Otsu method 0.051
Novel Bat Algorithm 53.83
Invasive Weed Optimization 412.71
Particle Swarm Optimization 36.93
362 images were processed with each algorithm and
the processing times were calculated for each image.
Later, the average time was calculated, these times
are presented in Table 1. It is observed that Otsu
method got a very small time to process each image.
It is relevant to mention that the Matlab
implementation of this algorithm was used. The
other algorithms were implemented and modified to
be used as segmentation algorithms.
In this work, a comparison of the bioinspired
algorithms applied to segmentation is presented,
something relevant given that most of these
algorithms are mainly designed for optimization.
We compared the errors calculated for each
segmentation performed by each bioinspired
The Potential Use of Bioinspired Algorithms Applied in the Segmentation of Mammograms
algorithm against the Otsu method, which is widely
used to perform image segmentation.
IWO algorithm got the lowest average error,
although it also got the highest time to process each
It is important to clarify that, in the medical field,
there must be a balance between time and quality,
since we are dealing with real patients and the
incorrect classification of a disease is a very
important factor.
The results presented in this article show a good
performance of the bioinspired algorithms in the task
of segmentation, so it is a good alternative to carry
out a more detailed analysis in this field.
As future work, we intend to carry out a more
detailed study and explore the possibility of
automatically adjusting the parameters of the
The authors gratefully acknowledge the CONACYT,
Instituto Politécnico Nacional, COFAA-IPN and
Honeywell for their economical support to develop
this work.
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