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