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 such elements. 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, which is based on the Differential Evolution (DE) algorithm, transforms the detection task into an optimization problem whose individuals represent candidate ellipses. An objective function evaluates if such candidate ellipses are actually present in the edge map 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 the WBC which are enclosed within the edge map 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.
The MatLAB code is an example of the implementation published in the paper:
Cuevas, E., Díaz, M., Manzanares, M., Zaldivar, D., Perez-Cisneros, M. An improved computer vision method for white blood cells detection, Computational and Mathematical Methods in Medicine, 2013 , art. no. 137392.
http://www.hindawi.com/journals/cmmm/2013/137392/
The MatLAB code involves 5 different files, DElipse, devec3, funcDE, 1399_full.jpg and mainElipseDetector. mainElipseDetector is the main function whereas the others are auxiliary functions.
The functions are briefly described as follows:
DElipse computes the parameters that involve the ellipse.
devec3 implements the DE algorithm
funcDE calculate the hypothetical points of each candidate ellipse.
1399_full.jpg is the RGB image containing the white blood cells
mainElipseDetector the main function
Run the example by typing:

[outellipses]=mainEllipseDetector('1399_full.jpg',0,110);

The generic function has the following format:

[outellipses]=mainEllipseDetector(image,thresmin,thresmax),

where,

Image >> It is the RGB image to be processed (it contains the white blood cells)
thresmin >>It is the low threshold value used in the segmentation process.
thresmax >>It is the high threshold value used in the segmentation process.