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In this paper we present a fully automated morphology-based technique for segmentation of nuclei in cancer tissue images and we compare it with a common technique for biomedical image processing, namely active contours. We discuss the limitations of active contours in the processing of immunohistochemical images characterized by heterogeneously stained nuclear region and noise caused by the presence of multiple tissue layers in the sample. We describe the integration of the proposed approach in a fully automated protein activity quantification tool. Finally, we demonstrate and motivate through extensive experiments that our fully automated morphology-based approach provides better accuracy compared to various active contours implementations.