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Title: Microparticle image processing and field profile optimisation for automated Lab-On-Chip magnetophoretic analytical systems
Author: Chowdhury, Mohammad M. U.
ISNI:       0000 0004 6346 657X
Awarding Body: University of Hull
Current Institution: University of Hull
Date of Award: 2016
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The work described in this thesis, concerns developments to analytical microfluidic Lab-On-Chip platform originally developed by Prof Pamme's research group at the University of Hull. This work aims to move away from traditional laboratory analysis system towards a more effective system design which is fully automated and therefore potentially deployable in applications such as point of care medical diagnosis. The microfluidic chip platform comprises an external permanent magnet and chip with multiple parallel reagent streams through which magnetic micro-particles pass in sequence. These streams may include particles, analyte, fluorescent labels and wash solutions; together they facilitate an on-chip multi-step analytical procedure. Analyte concentration is measured via florescent intensity of the exiting micro-particles. This has previously been experimentally proven for more than one analytical procedure. The work described here has addressed a couple of issues which needed improvement, specifically optimizing the magnetic field and automating the measurement process. These topics are related by the fact that an optimal field will reduce anomalies such as aggregated particles which may degrade automated measurements. For this system, the optimal magnetic field is homogeneous gradient of sufficient strength to pull the particles across the width of the device during fluid transit of its length. To optimise the magnetic field, COMSOL (a Multiphysics simulation program) was used to evaluate a number of multiple magnet configurations and demonstrate an improved field profile. The simulation approach was validated against experimental data for the original single-magnet design. To analyse the results automatically, a software tool has been developed using C++ which takes image files generated during an experiment and outputs a calibration curve or specific measurement result. The process involves detection of the particles (using image segmentation) and object tracking. The intensity measurement follows the same procedure as the original manual approach, facilitating comparison, but also includes analysis of particle motion behaviour to allow automatic rejection of data from anomalous particles (e.g. stuck particles). For image segmentation a novel texture based technique called Temporal- Adaptive Median Binary Pattern (T-AMBP) combining with Three Frame Difference method to model the background for representing the foreground was proposed. This proposed approached is based on previously developed Adaptive Median Binary Pattern (AMBP) and Gaussian Mixture Model (GMM) approach for image segmentation. The proposed method successfully detects micro-particles even when they have very low fluorescent intensity, while most of the previous approaches failed and is more robust to noise and artefacts. For tracking the micro-particles, we proposed a novel algorithm called "Hybrid Meanshift", which combines Meanshift, Histogram of oriented gradients (HOG) matching and optical flow techniques. Kalman filter was also combined with it to make the tracking robust. The processing of an experimental data set for generating a calibration curve, getting effectively the same results in less than 5 minutes was demonstrated, without needing experimental experience, compared with at least 2 hours work by an experienced experimenter using the manual approach.
Supervisor: Bell, Ian M. ; Pamme, Nicole Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Engineering