Omar Al-Kofahi

TL;DR: In this paper, the authors present 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.

TL;DR: A method for automated tracking of lineages of proliferative, migrating cells from a sequence of images that enables a level of quantitative analysis of cell behavior over time that was previously infeasible.

TL;DR: 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, accomplishing in minutes what it would take an expert hours to complete.

TL;DR: A practical two‐view method that increases the overall imaging depth, corrects signal attenuation and improves the image signal‐to‐noise ratio (SNR), and the reconstructed images are a more faithful qualitative visualization of the specimen's structure and are quantitatively more accurate, providing a more rigorous basis for automated image analysis.

TL;DR: In this article, high-order transformation models are used to register 3D confocal images of dye-injected neurons, and robust statistical methods are incorporated to achieve accurate registration in the face of inaccurate and missing landmarks.