Robust statistical models for cell image interpretation

摘要

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 models. Boundaries are represented using trainable statistical models that are invariant to transformations of scaling, shift, rotation and contrast; a Gaussian and a circular autoregressive (CAR) model 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 reliably be segmented. The results presented show that overall performance is comparable with that for manual segmentation; the area within the automatically and 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.