The assessment of megakaryocytes (MKs) in bone marrow trephine images is an important step in the classification of different subtypes of myeloproliferative neoplasms (MPNs). In general, bone marrow trephine images include several types of cells mixed together, which make it quite difficult to visually identify MKs. In order to aid hematopathologists in the identification and study of MKs, we develop an image processing framework with supervised machine learning approaches and a novel circumscribing active contour model to identify potential MKs and then to accurately delineate the corresponding nucleus and membrane. Specifically, a number of color and texture features are used in a nave Bayesian classifier and an Adaboost classifier to locate the regions with a high probability of depicting MKs. A region-based active contour is used on the candidate MKs to accurately delineate the boundaries of nucleus and membrane. The proposed circumscribing active contour model employs external forces not only based on pixel intensities, but also on the probabilities of depicting MKs as computed by the classifiers. Experimental results suggest that the machine learning approach can detect potential MKs with an accuracy of more than 75%. When our circumscribing active contour model is employed on the candidate MKs, the nucleus and membrane boundaries are segmented with an accuracy of more than 80% as measured by the Dice similarity coefficient. Compared to traditional region-based active contours, the use of additional external forces based on the probability of depicting MKs improves segmentation performance and computational time by an average 5%.