Automation of flow analysis in scleral vessels based on descriptive-associative algorithms.

Blood flow reflects the eye's health and is disrupted in many diseases. Many pathological processes take place at the cellular level like as microcirculation of blood in vessels, and the processing of medical images is a difficult recognition task. Existing techniques for measuring blood flow are limited due to the complex assumptions, equipment and calculations requirements. In this paper, we propose a method for determining the blood flow characteristics in eye conjunctiva vessels, such as linear and volumetric blood speed and topological characteristics of the vascular net. The method preprocesses the video to improve the conditions of analysis and then builds an integral optical flow for definition of flow dynamical characteristic of eye vessels. These characteristics make it possible to determine changes in blood flow in eye vessels. We show the efficiency of our method in natural eye vessel scenes. The research provides valuable insights to novices with limited experience in the diagnosis and can serve as a valuable tool for experienced medical professionals.

Development of fan-shaped tracker for single particle tracking.

With the development of super-resolution fluorescence microscopy, complex dynamic processes in living cells can be observed and recorded with unprecedented temporal and spatial resolution. Single particle tracking (SPT) is the most important step to explore the relationship between the spatio-temporal dynamics of subcellular molecules and their functions. Although previous studies have developed SPT algorithms to quantitatively analyze particle dynamics in cell, traditional tracking methods have poor performance when dealing with intersecting trajectories. This can be attributed to two main reasons: (a) they do not have point compensation process for overlapping objects; (b) they use inefficient motion prediction models. In this paper, we present a novel fan-shaped tracker (FsT) algorithm to reconstruct the trajectories of subcellular vesicles in living cells. We proposed a customized point compensation method for overlapping objects based on the fan-shaped motion trend of the particles. Furthermore, we validated the performance of the FsT in both simulated time-lapse movies with variable imaging quality and in real vesicle moving images. Meanwhile, we compared the performance of FsT with other five state-of-the-art tracking algorithms by using commonly defined measures. The results showed that our FsT achieves better performance in high signal-to-noise ratio conditions and in tracking of overlapping objects. We anticipate that our FsT method will have vast applications in tracking of moving objects in cell.