A system has been developed to perform automatic computerized recognition, tracking, and quantitative morphological analysis of viable cells in freezing solutions. Cryomicroscopical image sequences of freezing cells are digitized and analyzed by computer. Image-processing techniques are used which are insensitive to contrast fluctuations from image to image, and which perform well even in noisy, cluttered images. The generalized Hough transform is used for shape detection, and a heuristic graph-search boundary completion algorithm is applied for shape extraction. The extracted cell shape may be analyzed for changes in cross-sectional area, perimeter length, shape deformation, and other metrics of interest. Knowledge from the shape-extraction phase is used to form a prediction of what shape the cell will be in the next image frame, and thus what to look for in the next shape-detection phase. This combination of knowledge-feedback with a powerful shape-detection technique produces an automatic, dynamic shape-recognition scheme capable of accurate recognition and analysis of the cells regardless of how deformed they may become during the freezing sequence. Example performance of the system is illustrated for a series of micrographs of freezing granulocytes.