Automated image analysis in cellular microscopy has acquired a great importance, as a consequence of the large amount of visual information that is currently generated in numerous applications. Computer programs and algorithms to perform these tasks are in constant evolution, and evaluating both their performance and the validity of the results obtained through them, has a primary importance. In the specific case of image segmentation, evaluating the results by means of comparison to reference (ground-truth) images, demand the availability of images in which the segmentation outcome could be considered perfect. This requisite is very hard to accom-plish with real images using manual segmentation, due to the limitations inherent to human analysts. As a consequence, in order to evaluate segmentation algorithms in cellular micros-copy applications, simulation of realistic images that can serve as an adequate ground-truth has acquired a significant impor-tance. In this work, an algorithm is described and applied to simulate synthetic images in cellular microscopy, specifically those composed by erythrocytes in the field of haematology. In order to generate the erythrocytes, a basic algorithm based in ellipses was developed, that can experience various types of random variations. These variations allow adding realistic details to them, like noise and blur. As a result, simulated images were obtained, which can be used to evaluate the effec-tiveness of segmentation algorithms in this application, thanks to the fact that at the same time binary ideal ground-truth images are generated. Finally, some examples in evaluating segmentation algorithms using synthetic images are presented, to illustrate their application.