Peripheral blood smear is microscopically examining technique for blood samples from patients by painting special dyes in clinic laboratories. Blood diseases can be diagnosed by examining morphology, numbers and percentages of leukocyte, erythrocyte and thrombocyte cells in blood samples. However, this method is a considerably time-consuming process and requires an evaluation performed by a hematology specialist. It is not often provided a definitive assessment due to the expert's clinical experience and judgment during review. Although there are considerable studies about the segmentation of blood smear images in the literature, there is no method to segment all blood cells. In this study, a new segmentation algorithm is proposed, which automatically extracts leukocyte, erythrocyte and thrombocyte cells from peripheral blood smear images. Purpose of this study here is to make highly accurate and complete blood count. The algorithm treats each image as a universal set and represents each object in the image as a subset as a result of the applied operations. In the developed method, leukocytes and thrombocytes achieve better success than other studies. However, it has been observed that the average success rate of stacked erythrocytes decreases. Statistical tests of the developed method were performed using 200 blood smear images in experimental studies. According to the obtained results, it is seen that high accuracy (leukocyte 99.86%, thrombocyte 98.4%, erythrocyte 93.4%) and precision (leukocyte 94.77%, thrombocyte 90.14%, erythrocyte 95.88%) were achieved in all three blood cells. (C) 2017 The Authors. Published by IASE.