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White blood cells also known as leukocytes play a significant role in the diagnosis of different diseases. Digital image processing techniques have successfully contributed to analyze the cells, leading to more accurate and reliable systems for disease diagnosis. However, a high variability on cell shape, size, edge and localization complicates the data extraction process. Moreover, the contrast between cell boundaries and the image's background varies according to lighting conditions during the capturing process. On the other hand, Artificial Immune Systems (AIS) have been successfully applied to tackle numerous challenging optimization problems with remarkable performance in comparison to classical techniques. One of the most widely employed AIS approaches is the Clonal selection algorithm (CSA) which allows to proliferate candidate solutions (antibody) that better resolve a de- terminate objective function (antigen). This paper is thus focused on the segmentation, localization and measurement of leukocytes from other different components in blood smear images. The algorithm uses two different CSA systems, one for the segmentation phase and other for the leukocyte detection task. In the approach, the detection problem is considered to be similar to an optimization problem between the leukocyte and its best matched circle shape. On the other hand, Clonal selection algorithm (CSA) is one of the most widely employed AIS approaches. The algorithm uses the encoding of three points as candidate leukocytes over the edge image. An objective function evaluates if such candidate leukocytes are actually present in the edge image following the guidance of the objective function's values. A set of encoded candidate leukocytes are evolved using the CSA so that they can fit into the actual leucocytes shown by the edge map of the image. The obtained results in comparison with the state-of-the-art algorithms validate the efficiency of the proposed technique with regard to accuracy, speed, and robustness. [ABSTRACT FROM AUTHOR]