Hanqing Zhang

A fast and robust circle detection method using isosceles triangles sampling

Circle detection using randomized sampling has been developed in recent years to reduce computational intensity. However, randomized sampling is sensitive to noise that can lead to reduced accuracy and false-positive candidates. To improve on the robustness of randomized circle detection under noisy conditions this paper presents a new methodology for circle detection based upon randomized isosceles triangles sampling. It is shown that the geometrical property of isosceles triangles provides a robust criterion to find relevant edge pixels which, in turn, offers an efficient means to estimate the centers and radii of circles. For best efficiency, the estimated results given by the sampling from individual connected components of the edge map were analyzed using a simple clustering approach. To further improve on the accuracy we applied a two-step refinement process using chords and linear error compensation with gradient information of the edge pixels. Extensive experiments using both synthetic and real images have been performed. The results are compared to leading state-of-the-art algorithms and it is shown that the proposed methodology has a number of advantages: it is efficient in finding circles with a low number of iterations, it has high rejection rate of false-positive circle candidates, and it has high robustness against noise. All this makes it adaptive and useful in many vision applications. A new circle detection algorithm based on randomized sampling of isosceles triangles (ITs) is presented.Sampling of isosceles triangles provides distinctive probability distribution for circular shapes.A low amount of iterations are needed by using a simple clustering approch.The proposed algorithm has; short execution time, high accuracy and high robustness against noise.

UmUTracker: A versatile MATLAB program for automated particle tracking of 2D light microscopy or 3D digital holography data

We present a versatile and fast MATLAB program (UmUTracker) that automatically detects and tracks particles by analyzing video sequences acquired by either light microscopy or digital in-line holog ...

Detecting Bacterial Surface Organelles on Single Cells Using Optical Tweezers

Bacterial cells display a diverse array of surface organelles that are important for a range of processes such as intercellular communication, motility and adhesion leading to biofilm formation, infections, and bacterial spread. More specifically, attachment to host cells by Gram-negative bacteria are mediated by adhesion pili, which are nanometers wide and micrometers long fibrous organelles. Since these pili are significantly thinner than the wavelength of visible light, they cannot be detected using standard light microscopy techniques. At present, there is no fast and simple method available to investigate if a single cell expresses pili while keeping the cell alive for further studies. In this study, we present a method to determine the presence of pili on a single bacterium. The protocol involves imaging the bacterium to measure its size, followed by predicting the fluid drag based on its size using an analytical model, and thereafter oscillating the sample while a single bacterium is trapped by an optical tweezer to measure its effective fluid drag. Comparison between the predicted and the measured fluid drag thereby indicate the presence of pili. Herein, we verify the method using polymer coated silica microspheres and Escherichia coli bacteria expressing adhesion pili. Our protocol can in real time and within seconds assist single cell studies by distinguishing between piliated and nonpiliated bacteria.

Refining particle positions using circular symmetry

Particle and object tracking is gaining attention in industrial applications and is commonly applied in: colloidal, biophysical, ecological, and micro-fluidic research. Reliable tracking information is heavily dependent on the system under study and algorithms that correctly determine particle position between images. However, in a real environmental context with the presence of noise including particular or dissolved matter in water, and low and fluctuating light conditions, many algorithms fail to obtain reliable information. We propose a new algorithm, the Circular Symmetry algorithm (C-Sym), for detecting the position of a circular particle with high accuracy and precision in noisy conditions. The algorithm takes advantage of the spatial symmetry of the particle allowing for subpixel accuracy. We compare the proposed algorithm with four different methods using both synthetic and experimental datasets. The results show that C-Sym is the most accurate and precise algorithm when tracking micro-particles in all tested conditions and it has the potential for use in applications including tracking biota in their environment.

ToxTrac: A fast and robust software for tracking organisms

1. Behavioral analysis based on video recording is becoming increasingly popular within research fields such as; ecology, medicine, ecotoxicology, and toxicology. However, the programs available to ...

DSeg: A dynamic image segmentation program to extract backbone patterns for filamentous bacteria and hyphae structures

Motivation Quantitative image analysis of growing filamentous fungi and prokaryotes are important to detect and evaluate morphological effects of growth conditions, compounds and mutations. However, analysis of time-series image data is often limited by the ability of the algorithms to accurately segment structures that are complicated or if an organism is within a crowded population. To overcome these issues we present DSeg; an image analysis program designed to process time-series image data as well as single images to find multiple filamentous structures e.g., filamentous prokaryotes, yeasts and molds using a dynamic segmentation approach. DSeg automatically segments and analyzes objects, includes drift correction, and outputs statistical data such as persistence length, growth rate and growth direction. Availability and implementation DSeg is a free open-source program written in MATLAB. DSeg can be downloaded as a package from https://sourceforge.net/projects/dseg-software. Contact magnus.andersson@umu.se and hanqing.zhang@umu.se Supplementary information Supplementary data are available at online.

3D Printed Water-Soluble Scaffolds for Rapid Production of PDMS Micro-Fluidic Flow Chambers

We report a novel method for fabrication of three-dimensional (3D) biocompatible micro-fluidic flow chambers in polydimethylsiloxane (PDMS) by 3D-printing water-soluble polyvinyl alcohol (PVA) filaments as master scaffolds. The scaffolds are first embedded in the PDMS and later residue-free dissolved in water leaving an inscription of the scaffolds in the hardened PDMS. We demonstrate the strength of our method using a regular, cheap 3D printer, and evaluate the inscription process and the channels micro-fluidic properties using image analysis and digital holographic microscopy. Furthermore, we provide a protocol that allows for direct printing on coverslips and we show that flow chambers with a channel cross section down to 40 μm × 300 μm can be realized within 60 min. These flow channels are perfectly transparent, biocompatible and can be used for microscopic applications without further treatment. Our proposed protocols facilitate an easy, fast and adaptable production of micro-fluidic channel designs that are cost-effective, do not require specialized training and can be used for a variety of cell and bacterial assays. To help readers reproduce our micro-fluidic devices, we provide: full preparation protocols, 3D-printing CAD files for channel scaffolds and our custom-made molding device, 3D printer build-plate leveling instructions, and G-code.

A drag force interpolation model for capsule-shaped cells in fluid flows near a surface

We report an interpolation model to calculate the hydrodynamic force on tethered capsule-shaped cells in micro-fluidic flows near a surface. Our model is based on numerical solutions of the full Navier-Stokes equations for capsule-shaped objects considering their geometry, aspect ratio and orientation with respect to fluid flow. The model reproduced the results from computational fluid dynamic simulations, with an average error of <0.15 % for objects with an aspect ratio up to 5, and the model exactly reproduced the Goldman approximation of spherical objects close to a surface. We estimated the hydrodynamic force imposed on tethered Escherichia coli cells using the interpolation model and approximate models found in the literature, for example, one that assumes that E. coli is ellipsoid shaped. We fitted the 2D-projected area of a capsule and ellipsoid to segmented E. coli cells. We found that even though an ellipsoidal shape is a reasonable approximation of the cell shape, the capsule gives 4.4 % better agreement, a small difference that corresponds to 15 % difference in hydrodynamic force. In addition, we showed that the new interpolation model provides a significantly better agreement compared to estimates from commonly used models and that it can be used as a fast and accurate substitute for complex and computationally heavy fluid dynamic simulations. This is useful when performing bacterial adhesion experiments in parallel-plate flow channels. We include a MATLAB script that can track cells in a video time-series and estimate the hydrodynamic force using our interpolation formula.

ToxId : an efficient algorithm to solve occlusions when tracking multiple animals

Video analysis of animal behaviour is widely used in fields such as ecology, ecotoxicology, and evolutionary research. However, when tracking multiple animals, occlusion and crossing are problematic, especially when the identity of each individual needs to be preserved. We present a new algorithm, ToxId, which preserves the identity of multiple animals by linking trajectory segments using their intensity histogram and Hu-moments. We verify the performance and accuracy of our algorithm using video sequences with different animals and experimental conditions. The results show that our algorithm achieves state-of-the-art accuracy using an efficient approach without the need of learning processes, complex feature maps or knowledge of the animal shape. ToxId is also computationally efficient, has low memory requirements, and operates without accessing future or past frames.