The problem of cell counting is of tremendous importance in many clinical laboratory and pathology testing procedures, where an accurate cell count is vital for various diagnostic objectives, such as classifying disease developments and assessing drug effectiveness. Unfortunately, even today, this task remains mostly a manual endeavor, and depends on time-consuming human labor. Automated cell counting is clearly a desirable alternative, but has yet to fully emerge due to outstanding challenges and limitations in equipment cost and processing methods. To this end, a framework for counting lymphocytes from microscopic images is proposed in this paper. The framework requires modest equipment investment, consisting mostly of a standard microscope and a digital camera, while offering promising performance results. This is possible due to a robust image processing pipeline involved, with strategically designed color space, filtering and edge detection operations. The framework is validated on the publicly available ALL-IDB dataset, which is noted for its challenging nature due to various obstacles for image processing operations. Specifically, this work tackles the automated counting of lymphocytes from this dataset. Currently, the proposed framework is already capable of delivering accuracies of over 90%, with low computational complexity and execution time. Therefore, with further performance improvements, it should be expected to offer a compelling alternative for clinical testing procedures.