Leukocyte Detection Through an Evolutionary Method - Complex System Modelling and Control Through Intelligent Soft Computations - page 139

1 Introduction

Soft computing has emerged as a powerful tool for information processing, decision
making and knowledge management. The techniques of soft computing have been
successfully developed in areas such as neural networks, fuzzy systems and evo-
lutionary algorithms. It is predictable that in the near future soft computing will play
a more important role in tackling several engineering problems. Image processing is
a very important research area. Classical image processing methods often face great
dif

culties while dealing with images containing noise and distortions. Under such
conditions, the use of computational intelligence approaches has been recently
extended to address challenging real-world image processing problems.
On the other hand, medical image processing has become more and more
important in diagnosis with the development of medical imaging and computer
technique. Huge amounts of medical images are obtained by X-ray radiography, CT
and MRI. They provide essential information for ef cient and accurate diagnosis

based on advance computer vision techniques (Zhuang and Meng 2004 ; Scholl
et al. 2011 ).
White Blood Cells (WBC) also known as leukocytes play a signi

cant role in the
diagnosis of different diseases. Although computer vision techniques have suc-
cessfully contributed to generate new methods for cell analysis, which in turn, have
lead into more accurate and reliable systems for disease diagnosis. However, high
variability on cell shape, size, edge and localization, complicates the data extraction
process. Moreover, the contrast between cell boundaries and the image s back-

ground may vary due to unstable lighting conditions during the capturing process.
Many works have been conducted in the area of blood cell detection. In Wang
and Chu ( 2009 ) a method based on boundary support vectors is proposed to identify
WBC. In such approach, the intensity of each pixel is used to construct feature
vectors whereas a Support Vector Machine (SVM) is used for classi

'

cation and
segmentation. By using a different approach, Wu et al. in 2006 , developed an
iterative Otsu method based on the circular histogram for leukocyte segmentation.
According to such technique, the smear images are processed in the Hue-Satura-
tion-Intensity (HSI) space by considering that the Hue component contains most of
the WBC information. One of the latest advances in white blood cell detection
research is the algorithm proposed by Wang et al. in 2007 , which is based on the
fuzzy cellular neural network (FCNN). Although such method has proved suc-
cessful in detecting only one leukocyte in the image, it has not been tested over
images containing several white cells. Moreover, its performance commonly decays
when the iteration number is not properly de ned, yielding a challenging problem

itself with no clear clues on how to make the best choice.
Since white blood cells can be approximated with an ellipsoid form, computer
vision techniques for detecting ellipses may be used in order to recognize them.
Ellipse detection in real images is an open research problem since long time ago.
Several approaches have been proposed which traditionally fall under three cate-
gories: Symmetry-based, Hough transform-based (HT) and Random sampling.