Leukocyte Detection Through an Evolutionary Method - Complex System Modelling and Control Through Intelligent Soft Computations - page 153

Fig. 8 Resulting images of the first test after applying the WBC detector: a original image,
b image segmented by the DEM algorithm, c edge map and d the white detected blood cells
6 Comparisons to Other Methods
In this section, a comprehensive set of smear-blood test images is used to test the
performance of the proposed approach. We have applied the proposed DE-based
detector to test images in order to compare its performance to other WBC detection
algorithms 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 ). In all cases, the algorithms are tuned according to the value set
which is originally proposed by their own references.
6.1 Detection Comparison
To evaluate the detection performance of the proposed detection method, Table 2
tabulates the comparative leukocyte detection performance of the BSV approach, the
IOmethod, theWang algorithm, the BGA detector and the proposed method, in terms
of detection rates and false alarms. The experimental data set includes 50 images
which are collected from the ASH Image Bank ( http://imagebank.hematology.org/ ) .