One of the most important and error-prone tasks in biological image analysis is the segmentation of touching or overlapping cells. Particularly for optical microscopy, including transmitted light and confocal fluorescence microscopy, there is often no consistent discriminative information to separate cells that touch or overlap. It is desired to partition touching foreground pixels into cells using the binary threshold image information only, and optionally incorporating gradient information. The most common approaches for segmenting touching and overlapping cells in these scenarios are based on the watershed transform. We describe a new approach called pixel replication for the task of segmenting elliptical objects that touch or overlap. Pixel replication uses the image Euclidean distance transform in combination with Gaussian mixture models to better exploit practically effective optimization for delineating objects with elliptical decision boundaries. Pixel replication improves significantly on commonly used methods based on watershed transforms, or based on fitting Gaussian mixtures directly to the thresholded image data. Pixel replication works equivalently on both 2-D and 3-D image data, and naturally combines information from multi-channel images. The accuracy of the proposed technique is measured using both the segmentation accuracy on simulated ellipse data and the tracking accuracy on validated stem cell tracking results extracted from hundreds of live-cell microscopy image sequences. Pixel replication is shown to be significantly more accurate compared with other approaches. Variance relationships are derived, allowing a more practically effective Gaussian mixture model to extract cell boundaries for data generated from the threshold image using the uniform elliptical distribution and from the distance transform image using the triangular elliptical distribution.

The analysis of time-lapse images showing cells dividing to produce clones of related cells is an important application in biological microscopy. Imaging at the temporal resolution required to establish accurate tracking for vertebrate stem or cancer cells often requires the use of transmitted light or phase-contrast microscopy. Processing these images requires automated segmentation, tracking and lineaging algorithms. There is also a need for any errors in the automated processing to be easily identified and quickly corrected. We have developed LEVER, an open source software tool that combines the automated image analysis for phase-contrast microscopy movies with an easy-to-use interface for validating the results and correcting any errors. AVAILABILITY AND IMPLEMENTATION: LEVER is available free and open source, licensed under the GNU GPLv3. Details on obtaining and using LEVER are available at http://n2t.net/ark:/87918/d9rp4t CONTACT: [email protected].

A common problem in many areas of behavioral research is the analysis of the large volume of data recorded during the execution of the tasks being studied. Recent work has proposed the use of an automated method based on canonical sets to identify the most representative patterns in a large data set, and described an initial experiment in identifying canonical web-browsing patterns. However, there is a significant limitation to the method: it requires the similarity matrix to be symmetric, and thus can only be used for problems that can be modeled as unoriented topologies. In this paper we propose a novel enhancement to the method to support oriented topologies by allowing the similarity matrix to be nonsymmetric. We demonstrate the power of this new technique by applying the new method to find canonical lane changes in a driving simulator experiment.

While the collection of behavioral protocols has been common practice in human-computer interaction research for many years, the analysis of large protocol data sets is often extremely tedious and time-consuming, and automated analysis methods have been slow to develop. This paper proposes an automated method of protocol analysis to find canonical behaviors --- a small subset of protocols that is most representative of the full data set, providing a reasonable "big picture" view of the data with as few protocols as possible. The automated method takes advantage of recent algorithmic developments in computational vision, modifying them to allow for distance measures between behavioral protocols. The paper includes an application of the method to web-browsing protocols, showing how the canonical behaviors found by the method match well to sets of behaviors identified by expert human coders.