Pascal Bamford

Unsupervised cell nucleus segmentation with active contours

The task of segmenting cell nuclei from cytoplasm in conventional Papanicolaou (Pap) stained cervical cell images is a classical image analysis problem which may prove to be crucial to the development of successful systems which automate the analysis of Pap smears for detection of cancer of the cervix. Although simple thresholding techniques will extract the nucleus in some cases, accurate unsupervised segmentation of very large image databases is elusive. Conventional active contour models as introduced by Kass, Witkin and Terzopoulos (1988) offer a number of advantages in this application, but suffer from the well-known drawbacks of initialisation and minimisation. Here we show that a Viterbi search-based dual active contour algorithm is able to overcome many of these problems and achieve over 99% accurate segmentation on a database of 20 130 Pap stained cell images

Method for accurate unsupervised cell nucleus segmentation

To achieve the extreme accuracy rates demanded by applications in unsupervised automated cytology, it is frequently necessary to supplement the primary segmentation algorithm with a segmentation quality control system. The more robust the segmentation strategy, the less severe the data pruning need be at the segmentation validation stage. These issues are addressed as we describe our cell nucleus segmentation strategy which is able to achieve 100% accurate segmentation from a data set of 19946 cell nucleus images by automatically discarding the most difficult cell images. The automatic quality checking is applied to enhance-the performance of a robust energy minimisation based segmentation scheme which already achieved a 99.47% accurate segmentation rate.

A two-stage scene segmentation scheme for the automatic collection of cervical cell images

A segmentation process which emulates the human method of screening cervical smears is presented. The scheme consists of two distinct stages, one at low magnification using water immersion segmentation, followed by a search based dual active contour method at high magnification. Each technique and the overall method is explained and examples are given.

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 algorithms 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.