  The automatic detection of White Blood Cells (WBC) still remains as an
unsolved issue in medical imaging. The analysis of WBC images has engaged
researchers from fields of medicine and computer vision alike. Since WBC can be
approximated by an ellipsoid form, an ellipse detector algorithm may be
successfully applied in order to recognize them. This paper presents an
algorithm for the automatic detection of WBC embedded into complicated and
cluttered smear images that considers the complete process as a multi-ellipse
detection problem. The approach, based on the Differential Evolution (DE)
algorithm, transforms the detection task into an optimization problem where
individuals emulate candidate ellipses. An objective function evaluates if such
candidate ellipses are really present in the edge image of the smear. Guided by
the values of such function, the set of encoded candidate ellipses
(individuals) are evolved using the DE algorithm so that they can fit into the
WBC enclosed within the edge-only map of the image. Experimental results from
white blood cell images with a varying range of complexity are included to
validate the efficiency of the proposed technique in terms of accuracy and
robustness.
