Biomedical subjects

Omar Al-Kofahi

Publications and source records attributed to Omar Al-Kofahi.

3 recordsLinked to original sources

PubMed2006

Automated cell lineage construction: a rapid method to analyze clonal development established with murine neural progenitor cells.

Understanding cell lineage relationships is fundamental to understanding development, and can shed light on disease etiology and progression. We present a method for automated tracking of lineages of proliferative, migrating cells from a sequence of images. The method is applicable to image sequences gathered either in vitro or in vivo. Currently, generating lineage trees from progenitor cells over time is a tedious, manual process, which limits the number of cell measurements that can be practically analyzed. In contrast, the automated method is rapid and easily applied, and produces a wealth of measurements including the precise position, shape, cell-cell contacts, motility and ancestry of each cell in every frame, and accurate timings of critical events, e.g., mitosis and cell death. Furthermore, it automatically produces graphical output that is immediately accessible. Application to clonal development of mouse neural progenitor cells growing in cell culture reveals complex changes in cell cycle rates during neuron and glial production. The method enables a level of quantitative analysis of cell behavior over time that was previously infeasible.

Algorithms↗

PubMed2006

Automated semantic analysis of changes in image sequences of neurons in culture.

Quantitative studies of dynamic behaviors of live neurons are currently limited by the slowness, subjectivity, and tedium of manual analysis of changes in time-lapse image sequences. Challenges to automation include the complexity of the changes of interest, the presence of obfuscating and uninteresting changes due to illumination variations and other imaging artifacts, and the sheer volume of recorded data. This paper describes a highly automated approach that not only detects the interesting changes selectively, but also generates quantitative analyses at multiple levels of detail. Detailed quantitative neuronal morphometry is generated for each frame. Frame-to-frame neuronal changes are measured and labeled as growth, shrinkage, merging, or splitting, as would be done by a human expert. Finally, events unfolding over longer durations, such as apoptosis and axonal specification, are automatically inferred from the short-term changes. The proposed method is based on a Bayesian model selection criterion that leverages a set of short-term neurite change models and takes into account additional evidence provided by an illumination-insensitive change mask. An automated neuron tracing algorithm is used to identify the objects of interest in each frame. A novel curve distance measure and weighted bipartite graph matching are used to compare and associate neurites in successive frames. A separate set of multi-image change models drives the identification of longer term events. The method achieved frame-to-frame change labeling accuracies ranging from 85% to 100% when tested on 8 representative recordings performed under varied imaging and culturing conditions, and successfully detected all higher order events of interest. Two sequences were used for training the models and tuning their parameters; the learned parameter settings can be applied to hundreds of similar image sequences, provided imaging and culturing conditions are similar to the training set. The proposed approach is a substantial innovation over manual annotation and change analysis, accomplishing in minutes what it would take an expert hours to complete.

Algorithms↗

PubMed2005

Image change detection algorithms: a systematic survey.

Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms. It is hoped that our classification of algorithms into a relatively small number of categories will provide useful guidance to the algorithm designer.

Algorithms↗