Leukocyte Detection Through an Evolutionary Method - Complex System Modelling and Control Through Intelligent Soft Computations - page 141

survive in the next generation. Due to its simplicity, ease of implementation, fast
convergence, and robustness, the DE algorithm has gained much attention,
reporting a wide range of successful applications in the literature (Babu and
Munawar 2007 ; Mayer et al. 2005 ; Kannan et al. 2003 ; Chiou et al. 2005 ; Cuevas
et al. 2010 ).
This chapter presents an algorithm for the automatic detection of blood cell
images based on the DE algorithm. The proposed method uses the encoding of ve

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. Experimental evidence
shows the effectiveness of such method in detecting leukocytes despite complex
conditions. Comparison to the state-of-the-art WBC detectors on multiple images
demonstrates a better performance of the proposed method.
The main contribution of this study is the proposal of a new WBC detector
algorithm that ef

ciently recognize WBC under different complex conditions while
considering the whole process as an ellipse detection problem. Although ellipse
detectors based on optimization present several interesting properties, to the best of
our knowledge, they have not yet been applied to any medical image processing up
to date.
This chapter is organized as follows: Sect. 2 provides a description of the DE
algorithm while in Sect. 3 the ellipse detection task is fully explained from an
optimization perspective within the context of the DE approach. The complete
WBC detector is presented in Sect. 4 . Section 5 reports the obtained experimental
results whereas Sect. 6 conducts a comparison between state-of-the-art WBC
detectors and the proposed approach. Finally, in Sect. 7 , some conclusions are
drawn.

2 Differential Evolution Algorithm

In the proposed approach, the problem of WBC detection is faced as an optimi-
zation problem. As optimization tool, the differential evolution (DE) algorithm is
used to solve the detection problem. In this section, the main characteristics of DE
are discussed.
DE algorithm is a simple and direct search algorithm which is based on population
and aims for optimizing global multi-modal functions. DE employs the mutation
operator as to provide the exchange of information among several solutions.
There are various mutation base generators to de

ne the algorithm type. The
version of DE algorithm used in this work is known as rand-to-best/1/bin or ”

(Storn and Price 1995 ). DE algorithms begin by initializing a population of N p and
D-dimensional vectors considering parameter values that are randomly distributed

'' DE1