A novel method for detecting and counting overlapping tracks in SSNTD by image processing techniques

Cell counting and velocity algorithms for hydrodynamic study of unsteady biological flows in micro-channels

In this paper, the combination of two algorithms, a cell counting algorithm and a velocity algorithm based on a Digital Particle Image Velocimetry (DPIV) method, is presented to study the collective behavior of micro-particles in response to hydrodynamic stimuli. A wide experimental campaign was conducted using micro-particles of different natures and diameters (from 5 to 16 μ m ), such as living cells and silica beads. The biological fluids were injected at the inlet of a micro-channel with an external oscillating flow, and the process was monitored in an investigated area, simultaneously, through a CCD camera and a photo-detector. The proposed data analysis procedure is based on the DPIV-based algorithm to extrapolate the micro-particles velocities and a custom counting algorithm to obtain the instantaneous micro-particles number. The counting algorithm was easily integrated with the DPIV-based algorithm, to automatically run the analysis to different videos and to post-process the results in time and frequency domain. The performed experiments highlight the difference in the micro-particles hydrodynamic responses to external stimuli and the possibility to associate them with the micro-particles physical properties. Furthermore, in order to overcome the hardware and software requirements for the development of a real-time approach, it was also investigated the possibility to detect the flows by photo-detector signals as an alternative to camera acquisition. The photo-detector signals were compared with the velocity trends as a proof of concept for further simplification and speed-up of the data acquisition and analysis. The algorithm flexibility underlines the potential of the proposed methodology to be suitable for real-time detection in embedded systems.

Improved semi automatic approach to count the tracks on LR-115 film for monitoring of radioactive elements

An improved semi automatic technique for counting the tracks formed on LR-115 films with the advantages of simplicity and speed is reported. In this technique, a microscope with a Dino-Eye eyepiece camera is coupled to a PC equipped with a python compiler. After etching of the LR-115 film, 16 track images were taken to find the track density. The images generated were binarized before application of a Python algorithm. This process does not disfigure the original track and increase the spatial resolution. The batch process option in Jasc Paint Shop Pro was used to binarize the 16 images simultanously. The Python program automatically counts the total number of tracks formed on the 16 track images. This method was compared with manual counting and counting with the software program-Scion image to verify it. The results showed that the proposed method is reasonably good at counting the tracks. It is a faster and less time-consuming method, and will facilitate measurements of etched tracks in a variety of applications.