Automatic circle detection in digital images has been considered as an important and complex task for the computer vision community that has devoted a tremendous amount of research on seeking for an optimal circle detector. On the other hand, medical imaging is an important field of application of image processing algorithms. In particular, the analysis of blood cell images has taken a great importance for researchers in medicine and computer vision in recent years. Since blood cells can be approximated by a quasi-circular form, a circular detector algorithm may be applied. This chapter presents an algorithm for the automatic detection of blood cell images embedded into complicated and cluttered images with no consideration of the conventional Hough transform techniques. The approach is based on an evolutionary computation technique called the Electromagnetism-Like Optimization (EMO). The algorithm uses the encoding of three non-collinear points as candidate circles (<Emphasis Type="Italic">x</Emphasis>, <Emphasis Type="Italic">y</Emphasis>, <Emphasis Type="Italic">r</Emphasis>) over an edge-only image. Guided by the values of the objective function, the set of encoded candidate circles (charged particles) are evolved using the EMO algorithm so that they can fit into the blood cells on the edge map of the image. Experimental results from blood cell images with a varying range of complexity are included to validate the efficiency of the proposed technique regarding accuracy, speed, and robustness.