Counting malaria parasites with a two-stage EM based algorithm using crowsourced data.

Malaria eradication of the worldwide is currently one of the main WHO's global goals. In this work, we focus on the use of human-machine interaction strategies for low-cost fast reliable malaria diagnostic based on a crowdsourced approach. The addressed technical problem consists in detecting spots in images even under very harsh conditions when positive objects are very similar to some artifacts.

Crowdsourcing malaria parasite quantification: an online game for analyzing images of infected thick blood smears.

Background: There are 600,000 new malaria cases daily worldwide. The gold standard for estimating the parasite burden and the corresponding severity of the disease consists in manually counting the number of parasites in blood smears through a microscope, a process that can take more than 20 minutes of an expert microscopist's time.

Objective: This research tests the feasibility of a crowdsourced approach to malaria image analysis.

Image processing challenges in the creation of spatiotemporal gene expression atlases of developing embryos.

To properly understand and model animal embryogenesis it is crucial to obtain detailed measurements, both in time and space, about their gene expression domains and cell dynamics. Such challenge has been confronted in recent years by a surge of atlases which integrate a statistically relevant number of different individuals to get robust, complete information about their spatiotemporal locations of gene patterns. This paper will discuss the fundamental image analysis strategies required to build such models and the most common problems found along the way.

Towards a digital model of zebrafish embryogenesis. Integration of cell tracking and gene expression quantification.

We elaborate on a general framework composed of a set of computational tools to accurately quantificate cellular position and gene expression levels throughout early zebrafish embryogenesis captured over a time-lapse series of in vivo 3D images. Our modeling strategy involves nuclei detection, cell geometries extraction, automatic gene levels quantification and cell tracking to reconstruct cell trajectories and lineage tree which describe the animal development. Each cell in the embryo is then precisely described at each given time t by a vector composed of the cell 3D spatial coordinates (x; y; z) along with its gene expression level g.

Cyclic mathematical morphology in polar-logarithmic representation.

We propose in this paper to perform mathematical morphology operators in a geometric transformation of an image. As a result of this procedure, processing images with regular structuring elements in the transformed domain is equivalent to working with deformed structuring elements in the original representation. More specifically, the conversion into polar-logarithmic coordinates provides satisfying results in image analysis applied to round objects, if they are roughly origin-centered.