Bayesian analysis of cell nucleus segmentation by a Viterbi search based active contour

An image segmentation scheme is shown to be exceptionally successful through the application of high-level knowledge of the required image objects (cell nuclei). By tuning the algorithm’s single parameter, it is shown that the performance can be maximised for the dataset, but leads to individual failures that may require alternative choices. A second stage is introduced to process each of the resulting segmentations obtained by varying the parameter over the working range. This stage gives a Bayesian interpretation of the results which indicates the probable accuracy of each of the segmentations that can then be used to make a decision upon whether to accept or reject the segmentation.