According to the Nottingham Grading System for breast cancer grading, nuclear pleomorphism is one of the three criteria along with tubule formation and mitotic count taken into account in grading procedure. Nuclear pleomorphism is largely based on the information about variation of nuclei appearance, size, and shape. Nuclei extraction from breast cancer images is thus necessary for cancer grading, and has become one of the major problem in the domain of automatic image analysis. Recently, several papers have shown that stochastic Marked Point Processes are a promising tool for dealing with this kind of problems. In this paper, we will present visual and quantitative comparisons of results obtained with two Marked Point Process based models with two types of objects used, and analyse the advantages of each of them. We will first show a way to detect nuclei position and size using ellipse-shaped objects. Ellipses give a good approximation of nuclei shape size in a fast way. We will then use arbitrarily-shaped objects to delineate more precisely nuclei contours. As this method is a data driven method, we will discuss the best data energy to use for each kind of objects, based on common criteria of the nuclei in any cancer grade. Results are obtained using Haematoxylin and Eosin (H and E) stained breast cancer slide images. As appearance, size and shape may vary a lot depending on the cancer grade, we will present results for different grades and compare our methods for each of them. The quantitative quality of obtained results will be shown vie comparing with a ground truth segmentations given by pathologists.