Leukocyte Detection Through an Evolutionary Method - Complex System Modelling and Control Through Intelligent Soft Computations - page 149

4.1 Image Preprocessing

To employ the proposed detector, smear images must be preprocessed to obtain two
new images: the segmented image and its corresponding edge map. The segmented
image is produced by using a segmentation strategy whereas the edge map is
generated by a border extractor algorithm. Such edge map is considered by the
objective function to measure the resemblance of a candidate ellipse with an actual
WBC.
The goal of the segmentation strategy is to isolate the white blood cells (WBC

s)
from other structures such as red blood cells and background pixels. Information of
color, brightness and gradients are commonly used within a thresholding scheme to
generate the labels to classify each pixel. Although a simple histogram thresholding
can be used to segment the WBC

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s, at this work the Diffused Expectation-Maxi-
mization (DEM) has been used to assure better results (Boccignone et al. 2004 ).
DEM is an Expectation-Maximization (EM) based algorithm which has been
used to segment complex medical images (Boccignone et al. 2007 ). In contrast to
classical EM algorithms, DEM considers the spatial correlations among pixels as a
part of the minimization criteria. Such adaptation allows to segment objects in spite
of noisy and complex conditions. The method models an image as a

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finite mixture,
where each mixture component corresponds to a region class and uses a maximum
likelihood approach to estimate the parameters for each class, via the expectation
maximization (EM) algorithm, which is coupled to anisotropic diffusion over
classes in order to account for the spatial dependencies among pixels.
For the WBC s segmentation, it has been used the implementation of DEM

provided in ( 2012 ). Since the implementation allows to segment gray-level images
and color images, it can be used for operating over all smear images with no regard
about how each image has been acquired. The DEM has been con

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gured consid-

j 9 = 5 , ering three different classes (K = 3), g ð r h ik Þ¼r j h ik k ¼ 0 : 1 and m =10

iterations. These values have been found as the best con guration set according to

(Boccignone et al. 2004 ).
As a final result of the DEM operation, three different thresholding points are

obtained: the

rst corresponds to the WBC ' s, the second to the red blood cells

whereas the third represents the pixels classi ed as background. Figure 6 b presents

the segmentation results obtained by the DEM approach employed at this work
considering the Fig. 6 a as the original image.
Once the segmented image has been produced, the edge map is computed. The
purpose of the edge map is to obtain a simple image representation that preserves
object structures. The DE-based detector operates directly over the edge map in
order to recognize ellipsoidal shapes. Several algorithms can be used to extract the
edge map; however, at this work, the morphological edge detection procedure
(Gonzalez and Woods 1992 ) has been used to accomplish such a task. Morpho-
logical edge detection is a traditional method to extract borders from binary images
in which original images (I B ) are eroded by a simple structure element (I E ) com-
posed by a matrix-template of 3 × 3 with all its values equal to one. Then, the