Monitoring mesenchymal stem cell cultures using image processing and pattern recognition techniques

Stem cells have attracted tremendous interest in recent times due to their promise in providing innovative new treatments for a great range of currently debilitating diseases. This is due to their potential ability to regenerate and repair damaged tissue, and hence restore lost body function, in a manner beyond the body's usual healing process. Bone marrow-derived mesenchymal stem cells or bone marrow stromal cells are one type of adult stem cells that are of particular interest. Since they are derived from a living human adult donor, they do not have the ethical issues associated with the use of human embryonic stem cells. They are also able to be taken from a patient or other donors with relative ease and then grown readily in the laboratory for clinical application. Despite the attractive properties of bone marrow stromal cells, there is presently no quick and easy way to determine the quality of a sample of such cells. Presently, a sample must be grown for weeks and subject to various time-consuming assays, under the direction of an expert cell biologist, to determine whether it will be useful. Hence there is a great need for innovative new ways to assess the quality of cell cultures for research and potential clinical application. The research presented in this thesis investigates the use of computerised image processing and pattern recognition techniques to provide a quicker and simpler method for the quality assessment of bone marrow stromal cell cultures. In particular, aim of this work is to find out whether it is possible, through the use of image processing and pattern recognition techniques, to predict the growth potential of a culture of human bone marrow stromal cells at early stages, before it is readily apparent to a human observer. With the above aim in mind, a computerised system was developed to classify the quality of bone marrow stromal cell cultures based on phase contrast microscopy images. Our system was trained and tested on mixed images of both healthy and unhealthy bone marrow stromal cell samples taken from three different patients. This system, when presented with 44 previously unseen bone marrow stromal cell culture images, outperformed human experts in the ability to correctly classify healthy and unhealthy cultures. The system correctly classified the health status of an image 88% of the time compared to an average of 72% of the time for human experts. Extensive training and testing of the system on a set of 139 normal sized images and 567 smaller image tiles showed an average performance of 86% and 85% correct classifications, respectively. The contributions of this thesis include demonstrating the applicability and potential of computerised image processing and pattern recognition techniques to the task of quality assessment of bone marrow stromal cell cultures. As part of this system, an image normalisation method has been suggested and a new segmentation algorithm has been developed for locating cell regions of irregularly shaped cells in phase contrast images. Importantly, we have validated the efficacy of both the normalisation and segmentation method, by demonstrating that both methods quantitatively improve the classification performance of subsequent pattern recognition algorithms, in discriminating between cell cultures of differing health status. We have shown that the quality of a cell culture of bone marrow stromal cells may be assessed without the need to either segment individual cells or to use time-lapse imaging. Finally, we have proposed a set of features, that when extracted from the cell regions of segmented input images, can be used to train current state of the art pattern recognition systems to predict the quality of bone marrow stromal cell cultures earlier and more consistently than human experts.

Designing for engagement towards healthier lifestyles through food image sharing : the case of I8DAT

This paper introduces the underlying design concepts of I8DAT, a food image sharing application that has been developed as part of a three-year research project – Eat, Cook, Grow: Ubiquitous Technology for Sustainable Food Culture in the City (http://www.urbaninformatics .net/projects/food) – exploring urban food practices to engage people in healthier, more environmentally and socially sustainable eating, cooking, and growing food in their everyday lives. The key aim of the project is to produce actionable knowledge, which is then applied to create and test several accessible, user-centred interactive design solutions that motivate user-engagement through playful and social means rather than authoritative information distribution. Through the design and implementation processes we envisage to integrate these design interventions to create a sustainable food network that is both technical and socio-cultural in nature (technosocial). Our primary research locale is Brisbane, Australia, with additional work carried out in three reference cities with divergent geographic, socio-cultural, and technological backgrounds: Seoul, South Korea, for its global leadership in ubiquitous technology, broadband access, and high population density; Lincoln, UK, for the regional and peri-urban dimension it provides, and Portland, Oregon, US, for its international standing as a hub of the sustainable food movement.

Improved image quality for x-ray CT imaging of gel dosimeters

Purpose: This study provides a simple method for improving precision of x-ray computed tomography (CT) scans of irradiated polymer gel dosimetry. The noise affecting CT scans of irradiated gels has been an impediment to the use of clinical CT scanners for gel dosimetry studies. Method: In this study, it is shown that multiple scans of a single PAGAT gel dosimeter can be used to extrapolate a ‘zero-scan’ image which displays a similar level of precision to an image obtained by averaging multiple CT images, without the compromised dose measurement resulting from the exposure of the gel to radiation from the CT scanner. Results: When extrapolating the zero-scan image, it is shown that exponential and simple linear fits to the relationship between Hounsfield unit and scan number, for each pixel in the image, provides an accurate indication of gel density. Conclusions: It is expected that this work will be utilised in the analysis of three-dimensional gel volumes irradiated using complex radiotherapy treatments.

Pattern recognition using invariants defined from higher order spectra : 2-D image inputs

A new algorithm for extracting features from images for object recognition is described. The algorithm uses higher order spectra to provide desirable invariance properties, to provide noise immunity, and to incorporate nonlinearity into the feature extraction procedure thereby allowing the use of simple classifiers. An image can be reduced to a set of 1D functions via the Radon transform, or alternatively, the Fourier transform of each 1D projection can be obtained from a radial slice of the 2D Fourier transform of the image according to the Fourier slice theorem. A triple product of Fourier coefficients, referred to as the deterministic bispectrum, is computed for each 1D function and is integrated along radial lines in bifrequency space. Phases of the integrated bispectra are shown to be translation- and scale-invariant. Rotation invariance is achieved by a regrouping of these invariants at a constant radius followed by a second stage of invariant extraction. Rotation invariance is thus converted to translation invariance in the second step. Results using synthetic and actual images show that isolated, compact clusters are formed in feature space. These clusters are linearly separable, indicating that the nonlinearity required in the mapping from the input space to the classification space is incorporated well into the feature extraction stage. The use of higher order spectra results in good noise immunity, as verified with synthetic and real images. Classification of images using the higher order spectra-based algorithm compares favorably to classification using the method of moment invariants

Particulate texture image analysis with applications

Texture analysis and textural cues have been applied for image classification, segmentation and pattern recognition. Dominant texture descriptors include directionality, coarseness, line-likeness etc. In this dissertation a class of textures known as particulate textures are defined, which are predominantly coarse or blob-like. The set of features that characterise particulate textures are different from those that characterise classical textures. These features are micro-texture, macro-texture, size, shape and compaction. Classical texture analysis techniques do not adequately capture particulate texture features. This gap is identified and new methods for analysing particulate textures are proposed. The levels of complexity in particulate textures are also presented ranging from the simplest images where blob-like particles are easily isolated from their back- ground to the more complex images where the particles and the background are not easily separable or the particles are occluded. Simple particulate images can be analysed for particle shapes and sizes. Complex particulate texture images, on the other hand, often permit only the estimation of particle dimensions. Real life applications of particulate textures are reviewed, including applications to sedimentology, granulometry and road surface texture analysis. A new framework for computation of particulate shape is proposed. A granulometric approach for particle size estimation based on edge detection is developed which can be adapted to the gray level of the images by varying its parameters. This study binds visual texture analysis and road surface macrotexture in a theoretical framework, thus making it possible to apply monocular imaging techniques to road surface texture analysis. Results from the application of the developed algorithm to road surface macro-texture, are compared with results based on Fourier spectra, the auto- correlation function and wavelet decomposition, indicating the superior performance of the proposed technique. The influence of image acquisition conditions such as illumination and camera angle on the results was systematically analysed. Experimental data was collected from over 5km of road in Brisbane and the estimated coarseness along the road was compared with laser profilometer measurements. Coefficient of determination R2 exceeding 0.9 was obtained when correlating the proposed imaging technique with the state of the art Sensor Measured Texture Depth (SMTD) obtained using laser profilometers.

Reliability of near-infrared spectroscopy for measuring biceps brachii oxygenation during sustained and repeated isometric contractions

We examine the test-retest reliability of biceps brachii tissue oxygenation index (TOI) parameters measured by near-infrared spectroscopy during a 10-s sustained and a 30-repeated (1-s contraction, 1-s relaxation) isometric contraction task at 30% of maximal voluntary contraction (30% MVC) and maximal (100% MVC) intensities. Eight healthy men (23 to 33 yr) were tested on three sessions separated by 3 h and 24 h, and the within-subject reliability of torque and each TOI parameter were determined by Bland-Altman+/-2 SD limits of agreement plots and coefficient of variation (CV). No significant (P>0.05) differences between the three sessions were found for mean values of torque and TOI parameters during the sustained and repeated tasks at both contraction intensities. All TOI parameters were within+/-2 SD limits of agreement. The CVs for torque integral were similar between the sustained and repeated task at both intensities (4 to 7%); however, the CVs for TOI parameters during the sustained and repeated task were lower for 100% MVC (7 to 11%) than for 30% MVC (22 to 36%). It is concluded that the reliability of the biceps brachii NIRS parameters during both sustained and repeated isometric contraction tasks is acceptable.

Comparison between electrically evoked and voluntary isometric contractions for biceps brachii muscle oxidative metabolism using near-infrared spectroscopy

This study compared voluntary (VOL) and electrically evoked isometric contractions by muscle stimulation (EMS) for changes in biceps brachii muscle oxygenation (tissue oxygenation index, ΔTOI) and total haemoglobin concentration (ΔtHb = oxygenated haemoglobin + deoxygenated haemoglobin) determined by near-infrared spectroscopy. Twelve men performed EMS with one arm followed 24 h later by VOL with the contralateral arm, consisting of 30 repeated (1-s contraction, 1-s relaxation) isometric contractions at 30% of maximal voluntary contraction (MVC) for the first 60 s, and maximal intensity contractions thereafter (MVC for VOL and maximal tolerable current at 30 Hz for EMS) until MVC decreased ∼30% of pre-exercise MVC. During the 30 contractions at 30% MVC, ΔTOI decrease was significantly (P < 0.05) greater and ∼tHb was significantly (P < 0.05) lower for EMS than VOL, suggesting that the metabolic demand for oxygen in EMS is greater than VOL at the same torque level. However, during maximal intensity contractions, although EMS torque (∼40% of VOL) was significantly (P < 0.05) lower than VOL, ΔTOI was similar and ΔtHb was significantly (P < 0.05) lower for EMS than VOL towards the end, without significant differences between the two sessions in the recovery period. It is concluded that the oxygen demand of the activated biceps brachii muscle in EMS is comparable to VOL at maximal intensity. © Springer-Verlag 2009.

Aerial image analysis using spiking neural networks with application to power line corridor monitoring

Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distributor, currently spends over $80 million a year inspecting and managing vegetation that encroach on power line assets. Currently, most vegetation management programs for distribution systems are calendar-based ground patrol. However, calendar-based inspection by linesman is labour-intensive, time consuming and expensive. It also results in some zones being trimmed more frequently than needed and others not cut often enough. Moreover, it’s seldom practicable to measure all the plants around power line corridors by field methods. Remote sensing data captured from airborne sensors has great potential in assisting vegetation management in power line corridors. This thesis presented a comprehensive study on using spiking neural networks in a specific image analysis application: power line corridor monitoring. Theoretically, the thesis focuses on a biologically inspired spiking cortical model: pulse coupled neural network (PCNN). The original PCNN model was simplified in order to better analyze the pulse dynamics and control the performance. Some new and effective algorithms were developed based on the proposed spiking cortical model for object detection, image segmentation and invariant feature extraction. The developed algorithms were evaluated in a number of experiments using real image data collected from our flight trails. The experimental results demonstrated the effectiveness and advantages of spiking neural networks in image processing tasks. Operationally, the knowledge gained from this research project offers a good reference to our industry partner (i.e. Ergon Energy) and other energy utilities who wants to improve their vegetation management activities. The novel approaches described in this thesis showed the potential of using the cutting edge sensor technologies and intelligent computing techniques in improve power line corridor monitoring. The lessons learnt from this project are also expected to increase the confidence of energy companies to move from traditional vegetation management strategy to a more automated, accurate and cost-effective solution using aerial remote sensing techniques.

Single crystal Raman spectroscopy of selected arsenite, antimonite and hydroxyantimonate minerals

This thesis concentrates on the characterisation of selected arsenite, antimonite, and hydroxyantimonate minerals based on their vibrational spectra. A number of natural arsenite and antimonite minerals were studied by single crystal Raman spectroscopy in order to determine the contribution of bridging and terminal oxygen atoms to the vibrational spectra. A series of natural hydrated antimonate minerals was also compared and contrasted using single crystal Raman spectroscopy to determine the contribution of the isolated antimonate ion. The single crystal data allows each band in the spectrum to be assigned to a symmetry species. The contribution of bridging and terminal oxygen atoms in the case of the arsenite and antimonite minerals was determined by factor group analysis, the results of which are correlated with the observed symmetry species. In certain cases, synthetic analogues of a mineral and/or synthetic compounds isostructural or related to the mineral of interest were also prepared. These synthetic compounds are studied by non-oriented Raman spectroscopy to further aid band assignments of the minerals of interest. Other characterisation techniques include IR spectroscopy, SEM and XRD. From the single crystal data, it was found that good separation between different symmetry species is observed for the minerals studied.

Vibrational spectroscopy of the multi anion mineral zykaite Fe4(AsO4)(SO4)(OH)•15H2O – implications for arsenate removal

Some minerals are colloidal and are poorly diffracting . Vibrational spectroscopy offers one of the few methods for the assessment of the structure of these types of minerals. Among this group of minerals is zykaite with formula Fe4(AsO4)(SO4)(OH)•15H2O. The objective of this research is to determine the molecular structure of the mineral zykaite using vibrational spectroscopy. Raman and infrared bands are attributed to the AsO43-, SO42- and water stretching vibrations. The sharp band at 3515 cm-1 is assigned to the stretching vibration of the OH units. This mineral offers a mechanism for the formation of more crystalline minerals such as scorodite and bukovskyite. Arsenate ions can be removed from aqueous systems through the addition of ferric compounds such as ferric chloride. This results in the formation of minerals such as zykaite and pitticite (Fe3+,AsO4,SO4,H2O).