Cellular tracking in time-lapse phase contrast images

The quantitative analysis of live cells is a key issue in evaluating biological processes. The current clinical practice involves the application of a tedious and time consuming manual tracking procedure on large amount of data. As a result, automatic tracking systems are currently developed and evaluated. However, problems caused by cellular division, agglomeration, Brownian motion and topology changes are difficult issues that have to be accommodated by automatic tracking techniques. In this paper, we detail the development of a fully automated multi-target tracking system that is able to deal with Brownian motion and cellular division. During the tracking process our approach includes the neighbourhood relationship and motion history to enforce the cellular tracking continuity in the spatial and temporal domain. The experimental results reported in this paper indicate that our method is able to accurately track cellular structures in time-lapse data

Fuzzy spectral and spatial feature integration for classification of nonferrous materials in hyperspectral data

Hyperspectral data allows the construction of more elaborate models to sample the properties of the nonferrous materials than the standard RGB color representation. In this paper, the nonferrous waste materials are studied as they cannot be sorted by classical procedures due to their color, weight and shape similarities. The experimental results presented in this paper reveal that factors such as the various levels of oxidization of the waste materials and the slight differences in their chemical composition preclude the use of the spectral features in a simplistic manner for robust material classification. To address these problems, the proposed FUSSER (fuzzy spectral and spatial classifier) algorithm detailed in this paper merges the spectral and spatial features to obtain a combined feature vector that is able to better sample the properties of the nonferrous materials than the single pixel spectral features when applied to the construction of multivariate Gaussian distributions. This approach allows the implementation of statistical region merging techniques in order to increase the performance of the classification process. To achieve an efficient implementation, the dimensionality of the hyperspectral data is reduced by constructing bio-inspired spectral fuzzy sets that minimize the amount of redundant information contained in adjacent hyperspectral bands. The experimental results indicate that the proposed algorithm increased the overall classification rate from 44% using RGB data up to 98% when the spectral-spatial features are used for nonferrous material classification

A novel framework for tracking in-vitro cells in time-lapse phase contrast data

With the proliferation of modern microscopy imaging technologies the amount of data that has to be analysed by biologists is constantly increasing and as a result the development of automatic approaches that are able to track cellular structures in timelapse images has become an important field of research. The aim of this paper is to detail the development of a novel tracking framework that is designed to extract the cell motility indicators in phase-contrast image sequences. To address issues that are caused by nonstructured (random) motion and cellular agglomeration, cell tracking is formulated as a sequential process where the inter-frame cell association is achieved by assessing the variation in the local structures contained in consecutive frames of the image sequence. We have evaluated the proposed algorithm on dense phase contrast cellular data and the reported results indicate that the developed algorithm is able to accurately track MadinDarby Canine Kidney (MDCK) Epithelial Cells in image data that is characterised by low contrast and high level of noise

Left-ventricle myocardium segmentation using a coupled level-set with a priori knowledge

This paper presents a coupled level-set segmentation of the myocardium of the left ventricle of the heart using a priori information. From a fast marching initialisation, two fronts representing the endocardium and epicardium boundaries of the left ventricle are evolved as the zero level-set of a higher dimension function. We introduce a novel and robust stopping term using both gradient and region-based information. The segmentation is supervised both with a coupling function and using a probabilistic model built from training instances. The robustness of the segmentation scheme is evaluated by performing a segmentation on four unseen data-sets containing high variation and the performance of the segmentation is quantitatively assessed

Robust controller design: Recent emerging concepts for control of mechatronic systems

The recent industrial revolution puts competitive requirements on most manufacturing and mechatronic processes. Some of these are economic driven, but most of them have an intrinsic projection on the loop performance achieved in most of closed loops across the various process layers. It turns out that successful operation in a globalization context can only be ensured by robust tuning of controller parameter as an effective way to deal with continuously changing end-user specs and raw product properties. Still, ease of communication in non-specialised process engineering vocabulary must be ensured at all times and ease of implementation on already existing platforms is preferred. Specifications as settling time, overshoot and robustness have a direct meaning in terms of process output and remain most popular amongst process engineers. An intuitive tuning procedure for robustness is based on linear system tools such as frequency response and bandlimited specifications thereof. Loop shaping remains a mature and easy to use methodology, although its tools such as Hinf remain in the shadow of classical PID control for industrial applications. Recently, next to these popular loop shaping methods, new tools have emerged, i.e. fractional order controller tuning rules. The key feature of the latter group is an intrinsic robustness to variations in the gain, time delay and time constant values, hence ideally suited for loop shaping purpose. In this paper, both methods are sketched and discussed in terms of their advantages and disadvantages. A real life control application used in mechatronic applications illustrates the proposed claims. The results support the claim that fractional order controllers outperform in terms of versatility the Hinf control, without losing the generality of conclusions. The paper pleads towards the use of the emerging tools as they are now ready for broader use, while providing the reader with a good perspective of their potential

Cytoplasmic RNA in undifferentiated neural stem cells: A potential label-free raman spectral marker for assessing the undifferentiated status

Raman microspectroscopy (rms) was used to identify, image, and quantify potential molecular markers for label-free monitoring the differentiation status of live neural stem cells (NSCs) in vitro. Label-free noninvasive techniques for characterization of NCSs in vitro are needed as they can be developed for real-time monitoring of live cells. Principal component analysis (PCA) and linear discriminant analysis (LDA) models based on Raman spectra of undifferentiated NSCs and NSC-derived glial cells enabled discrimination of NSCs with 89.4% sensitivity and 96.4% specificity. The differences between Raman spectra of NSCs and glial cells indicated that the discrimination of the NSCs was based on higher concentration of nucleic acids in NSCs. Spectral images corresponding to Raman bands assigned to nucleic acids for individual NSCs and glial cells were compared with fluorescence staining of cell nuclei and cytoplasm to show that the origin of the spectral differences were related to cytoplasmic RNA. On the basis of calibration models, the concentration of the RNA was quantified and mapped in individual cells at a resolution of ∼700 nm. The spectral maps revealed cytoplasmic regions with concentrations of RNA as high as 4 mg/mL for NSCs while the RNA concentration in the cytoplasm of the glial cells was below the detection limit of our instrument (∼1 mg/mL). In the light of recent reports describing the importance of the RNAs in stem cell populations, we propose that the observed high concentration of cytoplasmic RNAs in NSCs compared to glial cells is related to the repressed translation of mRNAs, higher concentrations of large noncoding RNAs in the cytoplasm as well as their lower cytoplasm volume. While this study demonstrates the potential of using rms for label-free assessment of live NSCs in vitro, further studies are required to establish the exact origin of the increased contribution of the cytoplasmic RNA. © 2012 American Chemical Society

Biologically-inspired data decorrelation for hyperspectral imaging

Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classificatio

Analysis of interaction between the apicomplexan protozoan Toxoplasma gondii and host cells using label-free Raman Spectroscopy

Label-free imaging using Raman micro-spectroscopy (RMS) was used to characterize the spatio-temporal molecular changes of T. gondii tachyzoites and their host cell microenvironment. Raman spectral maps were recorded from isolated T. gondii tachyzoites and T. gondii-infected human retinal cells at 6 hr, 24 hr and 48 hr post-infection. Principal component analysis (PCA) of the Raman spectra of paraformaldehyde-fixed infected cells indicated a significant increase in the amount of lipids and proteins in the T. gondii tachyzoites as the infection progresses within host cells. These results were confirmed by experiments carried out on live T. gondii-infected cells and were correlated with an increase in the concentration of proteins and lipids required for the replication of this intracellular pathogen. These findings demonstrate the potential of RMS to characterize time- and spatially-dependent molecular interactions between intracellular pathogens and the host cells. Such information may be useful for discovery of pharmacological targets or screening compounds with potential neuro-protective activity for eminent effects of changes in brain infection control practices