The paper presents a method for automatic segmentation of sputum cells color images, to develop an efficient algorithm for lung cancer diagnosis based on a Hopfield neural network. We formulate the segmentation problem as a minimization of an energy function constructed with two terms, the cost-term as a sum of squared errors, and the second term a temporary noise added to the network as an excitation to escape certain local minima with the result of being closer to the global minimum. To increase the accuracy in segmenting the regions of interest, a preclassification technique is used to extract the sputum cell regions within the color image and remove those of the debris cells. The proposed technique has yielded correct segmentation of complex scene of sputum prepared by ordinary manual staining method in most of the tested images selected from our database containing thousands of sputum color images.