Manual and Automatic Image Analysis Segmentation Methods for Blood Flow Studies in Microchannels

In blood flow studies, image analysis plays an extremely important role to examine raw data obtained by high-speed video microscopy systems. This work shows different ways to process the images which contain various blood phenomena happening in microfluidic devices and in microcirculation. For this purpose, the current methods used for tracking red blood cells (RBCs) flowing through a glass capillary and techniques to measure the cell-free layer thickness in different kinds of microchannels will be presented. Most of the past blood flow experimental data have been collected and analyzed by means of manual methods, that can be extremely reliable, but they are highly time-consuming, user-intensive, repetitive, and the results can be subjective to user-induced errors. For this reason, it is crucial to develop image analysis methods able to obtain the data automatically. Concerning automatic image analysis methods for individual RBCs tracking and to measure the well known microfluidic phenomena cell-free layer, two developed methods are presented and discussed in order to demonstrate their feasibility to obtain accurate data acquisition in such studies. Additionally, a comparison analysis between manual and automatic methods was performed.

Semi-automatic image analysis of spatiotemporal vegetation evolution in the Hühnerwasser catchment

<p><strong>The artificial Hühnerwasser catchment was built in a post-mining landscape </strong><strong>(Brandenburg, Germany)</strong><strong> as a field experiment to observe and monitor early-development ecosystems at first catchment scale. </strong><strong>Given that the </strong><strong>spatial distribution and temporal dynamics of vegetation affects water redistribution </strong><strong>across scales, </strong><strong>quantifying changes in vegetation </strong><strong>distributions </strong><strong>is an obvious indicator for state transitions, </strong><strong>especially in the context of early ecosystem development</strong><strong>. </strong></p><p> </p><p><strong>In this work, we present a semi-automatic image analysis algorithm designed to</strong> <strong>identify vegetation patches during the early ecosystem development of the Hühnerwasser catchment (throughout 10 years) from aerial photography. Furthermore, the algorithm also allows to </strong><strong>characteri</strong><strong>se</strong><strong> vegetation cover,</strong> <strong>describe spatial structures and </strong><strong>their temporal evolution. </strong><strong>The earl</strong><strong>iest</strong><strong> stages are especially of interest. The structure is therefore characterized by the area of the catchment covered by vegetation, the number of vegetation patches, the mean and maximum patch size and a form factor (area of patch divided by its perimeter). </strong><strong>Base data are a</strong><strong>erial images with a resolution at</strong><strong> the centimeter </strong><strong>scale. </strong><strong>Because the imagery was obtained under very different lighting conditions and under different stages of plant growth, a luminance correction was applied in order to normalise colors, and thus be able of consistently binarise the images into vegetated-non vegetated maps. </strong><strong>Binary maps </strong><strong>were</strong><strong> generated by setting thresholds for red, green and blue channels to differentiate between vegetation cover and bare soil. </strong><strong>Additionally, bare soil areas were also identified using a similar procedure. </strong><strong>To evaluate the consistency of the binary images of each channel these images were stacked and compared. </strong><strong>For validation, the binary maps were compared to manually digitised vegetation patches for a subset of the data. </strong><strong>The performance of the method was tested by using a set of combinations of thresholds and a comparison with manual mapping of vegetation cover at an image subset was made. </strong></p><p> </p><p><strong>The blue channel seems to be very sensitive to detect vegetation and a better differentiation of vegetation and dark/wet soil can be achieved by setting the thresholds of the channels in a specific order.</strong> <strong>The structures derived by the classification into vegetated and bare soil are more important in the early years of ecosystem development. In those years (2007 to 2011) </strong><strong>the largest</strong><strong> changes took place. As time advances vegetation became less patchy, </strong><strong>and a mix of different vegetation spawns. By comparing the areas identified as (green) vegetation and those areas identified as bare soil, it is also possible to discriminate non-green vegetation, such as dry grasses, and thus achieve a minimal level of decomposition of the imagery into</strong><strong> plant functional types.</strong></p><p> </p>

Geometric Semi-automatic Analysis of Colles’ Fractures

Fractures of the wrist are common in Emergency Departments, where some patients are treated with a procedure called Manipulation under Anaesthesia. In some cases this procedure is unsuccessful and patients need to visit the hospital again where they undergo surgery to treat the fracture. This work describes a geometric semi-automatic image analysis algorithm to analyse and compare the x-rays of healthy controls and patients with dorsally displaced wrist fractures (Colles’ fractures) who were treated with Manipulation under Anaesthesia. A series of 161 posterior-anterior radiographs from healthy controls and patients with Colles’ fractures were acquired and analysed. The patients’ group was further subdivided according to the outcome of the procedure (successful/unsuccessful) and pre- or post-intervention creating five groups in total (healthy, pre-successful, pre-unsuccessful, post-successful, post-unsuccessful). The semi-automatic analysis consisted of manual location of three landmarks (finger, lunate and radial styloid) and automatic processing to generate 32 geometric and texture measurements, which may be related to conditions such as osteoporosis and swelling of the wrist. Statistical differences were found between patients and controls and pre- and post-intervention, but not between the procedures. The most distinct measurements were those of texture. Although the study includes a relatively low number of cases and measurements, the statistical differences are encouraging.