Robust Statistical Model-Based Cell Image Interpretation.

A robust and adaptable model-based scheme for cell image interpretation is presented that can accommodate the wide natural variation in the appearance of cells. This is achieved using multiple models and an interpretation process that permits a smooth transition between the models. Boundaries are represented using trainable statistical models that are invariant to transformatio ns of scaling, shift, rotation and contrast; a Gaussian and a circular autoregressive model (CAR) are investigated. The interpretation process optimises the match between models and data using a Bayesian distance measure. We demonstrate how objects that vary in both shape and grey-level pattern can be reliably segmented. The results presented show that the overall performance is comparable with that of manual segmentation; the area within the automatically detected and the manually selected cell boundaries that is not common to both is less than 5% in 96% of the cases tested. The results also show that the computationally simpler Gaussian boundary model is at least as effective as the CAR model.