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Lung cancer has been the largest cause of cancer deaths worldwide with an overall 5-year survival rate of only 15%. Its early detection significantly increases the chances of an effective treatment. For this purpose, a computer-aided design system using images of sputum stained smears is a practical, low-cost, and totally non invasive solution. In this paper, we present a framework for the detection and segmentation of sputum cells in sputum images using respectively, a Bayesian classification and mean shift segmentation. Our methods are validated and compared with other competitive approaches via a series of experiments conducted with a data set of 88 images.