Computational Intelligence - February 2013 - 32

1.0

250

0.8

200

0.6

150

0.4

100

obtained segmentations were not fully correct. To better take
advantage of the region-based segmentation capabilities, we
could endow the ETAN with a texture analysis system. Indeed,
texture is one of the important characteristics used in identifying objects or regions of interest in an image.
Acknowledgment

50

0.2

0
LS

MS DE
(a)

SS

LS

MS DE
(b)

SS

Figure 19 The distributions of the results (1/10 runs for 20 images),
for S and dH metrics obtained by the four methods. (a) Index S and
(b) dHausdorff.

❏ H0: median difference between the pairs is zero
❏ H1: median difference is not zero.

The p-values obtained for the four metrics are shown
in Table 3. For SS, the values of the medians are higher for the
S index and lower for the three distances. Given the obtained
p-values, we found enough evidence to reject the null hypothesis with a confidence level of 0.05 for every metric. It is worth
noting that the confidence level could have been much lower
for the S, M dRT and dH metrics, for example 0.01.
Although the Wilcoxon test is significant enough to prove
the superiority of the performance of our proposal with
respect to the other three methods, for the sake of clarity we
also show a boxplot of the two most relevant metrics, S and
dH, in Fig. 19. These boxplots are a quick way to graphically
examine graphically the distributions of the 200 results
obtained by the three stochastic algorithms over the 20 images,
considering the 10 runs (being deterministic, LS has been run
just once per image).
V. Conclusions and Future Work

In this work we have proposed an accurate, robust and automatic segmentation method that is able to perform in a reasonable time. It embeds the ETAN model into a customized SS
global search framework. We designed several new specific
components for the method. They become a crucial outcome
allowing us to really take advantage of the population-based
optimization framework as none of the previous approaches
were able to do. The obtained results were encouraging. Our
SS proposal significantly improved the accuracy of the segmentation on real-world medical images, as well as on synthetic
ones, in comparison with the original ETAN-EBILS, an
ETAN-MSLS and the state-of-the-art DE-based MA for
TANs. Moreover, the robustness achieved was significantly better than the previous methods.
Nevertheless, regardless of its good performance, our
method still has room for improvement. In a few cases the

32

IEEE ComputatIonal IntEllIgEnCE magazInE | FEbruary 2013

This work is supported by the European Commission with the
contract No. 238819 (MIBISOC Marie Curie ITN) and by
the Spanish Ministerio de Educación y Ciencia (ref. TIN200907727), both including EDRF funds. Part of the code related
to DE was provided by J. Novo and the VARPA group, University of A Coruña, Spain. We would like to thank A.Valsecchi
for his support in performing the statistical analysis.
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