The marked point process (MPP) provides a useful and theoretically well-established tool for integrating spatial information into the image analysis process. We consider the problem of detecting rolling leukocytes within intravital microscopy images. A first stage of the detection method reduces the detection to a set of points, each one representing a possible leukocyte. Our task is then to decide which points are actual leukocytes. We propose an MPP-based approach that aims at improving both the accuracy and efficiency of the detection process by means of exploiting the spatial interrelationships. We construct a Markov chain Monte Carlo algorithm to obtain the maximum a posteriori (MAP) estimation of a set of points corresponding to the centroids of leukocytes observed in the image. The optimal solution, in terms of the MAP principle, is computed with respect to all leukocytes, rather than a single leukocyte. A quantitative study of our detection approach demonstrates results that compare very well to those achieved by manual detection and exceed the solution quality given by two competing methods. Our approach can serve as a fully automated substitute to the tedious and time-consuming manual rolling leukocyte detection process.