Research Article An Improved Computer Vision Method for

Copyright © 2013 Erik Cuevas et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The automatic detection of white blood cells (WBCs) 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. SinceWBC can be approximated by an ellipsoid form, an ellipse detector algorithmmaybe successfully applied in order to recognize such elements.This paper presents an algorithm for the automatic detection ofWBC embedded in complicated and cluttered smear images that considers the complete process as a multiellipse detection problem.The approach, which is based on the differential evolution (DE) algorithm, transforms the detection task into an optimization problemwhose individuals represent candidate ellipses. An objective function evaluates if such candidate ellipses are actually present in the edgemap of the smear image. 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 theWBCs which are enclosed within the edgemap of the smear 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 its accuracy and robustness. 1