Leukocyte Detection Through an Evolutionary Method - Complex System Modelling and Control Through Intelligent Soft Computations - page 160

160

140

120

Wang
DE−based

100

80

60

40

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0

50 100 150 200 250 300 350 400 450 500 550 600

Iterations

Fig. 13 Error percentage versus iterations evolution from an extended data set from Table 5

7 Conclusions

In this chapter, an algorithm for the automatic detection of blood cell images based
on the DE algorithm has been presented. The approach considers the complete
process as a multiple ellipse detection problem. The proposed method uses the
encoding of five edge points as candidate ellipses in the edge map of the smear. An

objective function allows to accurately measure the resemblance of a candidate
ellipse with an actual WBC on the image. Guided by the values of such objective
function, the set of encoded candidate ellipses are evolved using the DE algorithm
so that they can

fit into actual WBC on the image. The approach generates a sub-
pixel detector which can effectively identify leukocytes in real images.
The performance of the DE-method has been compared to other existing WBC
detectors such as the Boundary Support Vectors (BSV) approach (Wang and Chu
2009 ), the iterative Otsu (IO) method (Wu et al. 2006 ), the Wang algorithm (Wang
et al. 2007 ) and the Genetic algorithm-based (GAB) detector (Karkavitsas and
Rangoussi 2005 ) considering several images which exhibit different complexity
levels. Experimental results demonstrate the high performance of the proposed
method in terms of detection accuracy, robustness and stability.

References

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Ayala-Ramirez, V., Garcia-Capulin, C. H., Perez-Garcia, A., & Sanchez-Yanez, R. E. (2006).
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Babu, B., & Munawar, S. (2007). Differential evolution strategies for optimal design of shell-and-
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