Understanding the mechanisms behind the proliferation of Mesenchymal Stem cells (MSCs) can offer a greater insight into the behaviour of these cells throughout their life cycles. Traditional methods of determining the rate of MSC differentiation rely on population based studies over an extended time period. However, such methods can be inadequate as they are unable to track cells as they interact; for example, in autologous cell therapies for osteoarthritis, the development of biological assays that could predict in vivo functional activity and biological action are particularly challenging. Here further research is required to determine non-histochemical biomarkers which provide correlations between cell survival and predictive functional outcome. This paper proposes using a (previously developed) advanced texture-based analysis algorithm to facilitate in vitro cells tracking using time-lapsed microscopy. The technique was adopted to monitor stem cells in the context of unlabelled, phase contrast imaging, with the goal of examining the cell to cell interactions in both monoculture and co-culture systems. The results obtained are analysed using established exploratory procedures developed for time series data and compared with the typical fluorescent-based approach of cell labelling. A review of the progress and the lessons learned are also presented.
A principal focus highlighting recent advances in cell based therapies concerns the development of effective treatments for osteoarthritis. Earlier clinicaltrials have shown that 80% of patients receiving <i>mesenchymal stem cell</i>(MSC) based treatment have improved their quality of life by alleviating pain whilst extending the life of their natural joints. The current challenge facing researchers is to identify the biological differences between the treatments that have worked and those which have shown little improvement. One possible candidate for the difference in treatment prognosis is an examination of the proliferation of the ( type) cells as they grow. To further understanding of the proliferation and differentiation of MSC, non-invasive live cell imaging techniques have been developed which capture important cell events and dynamics in cell divisions over an extended period of time. An automated image analysis procedure capable of tracking cell confluence over time has also been implemented, providing an objective and realistic estimation of cell growth within continuous live cell cultures. The proposed algorithm accounts for the halo artefacts that occur in phase microscopy. In addition to a favourable run-time performance, the method was also validated using continuous live MSC cultures, with consistent and meaningful results.
Within the field of tissue engineering there is an emphasis on studying 3-D live tissue structures. Consequently, to investigate and identify cellular activities and phenotypes in a 3-D environment for all in vitro experiments, including shape, migration/proliferation and axon projection, it is necessary to adopt an optical imaging system that enables monitoring 3-D cellular activities and morphology through the thickness of the construct for an extended culture period without cell labeling. This paper describes a new 3-D tracking algorithm developed for Cell-IQ®, an automated cell imaging platform, which has been equipped with an environmental chamber optimized to enable capturing time-lapse sequences of live cell images over a long-term period without cell labeling. As an integral part of the algorithm, a novel auto-focusing procedure was developed for phase contrast microscopy equipped with 20x and 40x objectives, to provide a more accurate estimation of cell growth/trajectories by allowing 3-D voxels to be computed at high spatiotemporal resolution and cell density. A pilot study was carried out in a phantom system consisting of horizontally aligned nanofiber layers (with precise spacing between them), to mimic features well exemplified in cellular activities of neuronal growth in a 3-D environment. This was followed by detailed investigations concerning axonal projections and dendritic circuitry formation in a 3-D tissue engineering construct. Preliminary work on primary animal neuronal cells in response to chemoattractant and topographic cue within the scaffolds has produced encouraging results.
Multivariate analysis seeks to describe the relationship between an arbitrary number of variables. To explore highdimensional
data sets, projections are often used for data visualisation to aid discovering structure or patterns that lead to
the formation of statistical hypothesis. The basic concept necessitates a systematic search for lower-dimensional
representations of the data that might show interesting structure(s). Motivated by the recent research on the Image Grand
Tour (IGT), which can be adapted to view guided projections by using objective indexes that are capable of revealing
latent structures of the data, this paper presents a signal processing perspective on constructing such indexes under the
unifying exploratory frameworks of Independent Component Analysis (ICA) and Projection Pursuit (PP). Our
investigation begins with an overview of dimension reduction techniques by means of orthogonal transforms, including
the classical procedure of Principal Component Analysis (PCA), and extends to an application of the more powerful
techniques of ICA in the context of our recent work on non-destructive testing technology by element specific x-ray
Parallel processing promises scalable and effective computing power which can handle the complex data structures of
knowledge representation languages efficiently. Past and present sequential architectures, despite the rapid advances in
computing technology, have yet to provide such processing power and to offer a holistic solution to the problem. This
paper presents a fresh attempt in formulating alternative techniques for grammar learning, based upon the parallel and
distributed model of <i>connectionism</i>, to facilitate the more cognitively demanding task of pattern understanding. The
proposed method has been compared with the contemporary approach of shape modelling based on level sets, and
demonstrated its potential as a prototype for constructing robust networks on high performance parallel platforms.
This paper investigates the method for object fingerprinting in the context of element specific x-ray imaging. In
particular, the use of spectral descriptors that are illumination invariant and viewpoint independent for pattern
identification was examined in some detail. To improve generating the relevant "signature", the spectral descriptor
constructed is enhanced with a differentiator which has built-in noise filtration capability and good localisation
properties, thus facilitating the extraction of element specific features at a coarse-grained level. In addition to the
demonstrable efficacy in identifying significant image intensity transitions that are associated with the underlying
physical process of interest, the method has the distinct advantage of being conceptually simple and computationally
efficient. These latter properties allow the descriptor to be further utilised by an intelligent system capable of performing
a fine-grained analysis of the extracted pattern signatures. The performance of the spectral descriptor has been studied in
terms of the quality of the signature vectors that it generated, quantitatively based on the established framework of
Spectral Information Measure (SIM). Early results suggested that such a multiscale approach of image sequence analysis
offers a considerable potential for real-time applications.
Moments are one of the most well known feature descriptors which can be extracted from an image; their mathematical properties and versatility as feature extractors are well studied. This paper presents a design of moment generators, using established techniques in digital filters and Very Large Scale Integration processing combined under a component-based design framework. Analytically, the moment generator architecture is constructed by cascading single- pole stages of a relatively simple filter suitable for implementation on an ASIC platform, and which is capable of producing a linear combination of moments. Individual set of moments can be extracted, by using dematrixing techniques which could also be realized in the form of a preprogrammable logic table. A parallel implementation of the design is described using C*, a data-parallel extension of ANSI C. Preliminary evaluation of the design and implementation is also presented.
The ability to simplify an image whilst retaining such crucial information as shapes and geometric structures is of great importance for real-time image analysis applications. Here the technique of binary thresholding which reduces the image complexity has generally been regarded as one of the most valuable methods, primarily owing to its ease of design and analysis. This paper studies the state of developments in the field, and describes a radically different approach of adaptive thresholding. The latter employs the analytical technique of histogram normalization for facilitating an optimal `contrast level' of the image under consideration. A suitable criterion is also developed to determine the applicability of the adaptive processing procedure. In terms of performance and computational complexity, the proposed algorithm compares favorably to five established image thresholding methods selected for this study. Experimental results have shown that the new algorithm outperforms these methods in terms of a number of important errors measures, including a consistently low visual classification error performance. The simplicity of design of the algorithm also lends itself to efficient parallel implementations.
Edge detection is an important first step in many vision tasks where its improvements in speed and efficiency present a continuous challenge for developers of high-speed image recognizers. Classical techniques for accurate detection of edge features, as exemplified by Canny operator, demands such expensive operations as the iterative use of Gaussians and Laplacians, and their designs are largely sequential. Alternatively a variety of complex and edge-preserving filters have been developed to reduce the effects of noise without significantly distorting the edge loci. This paper describes a cascaded precursor approach for edge detection based on selective local contrast modifications which combine point- wise image operators and non-linear transformation. A principal advantage of the approach lies in its simplicity and uniformity of operations; the latter is a characteristic blueprint for efficient (parallel) low-level image processing algorithms. Further, unlike many enhancement algorithms, the characteristics of the proposed precursor can be studied analytically, thus allowing the independent adjustments of detector parameters for maximum performance in the specific environment.