Segmentation of phase contrast microscopy images based on multi-scale local Basic Image Features histograms

Phase contrast microscopy (PCM) is routinely used for the inspection of adherent cell cultures in all fields of biology and biomedicine. Key decisions for experimental protocols are often taken by an operator based on typically qualitative observations. However, automated processing and analysis of PCM images remain challenging due to the low contrast between foreground objects (cells) and background as well as various imaging artefacts. We propose a trainable pixel-wise segmentation approach whereby image structures and symmetries are encoded in the form of multi-scale Basic Image Features local histograms, and classification of them is learned by random decision trees. This approach was validated for segmentation of cell versus background, and discrimination between two different cell types. Performance close to that of state-of-the-art specialised algorithms was achieved despite the general nature of the method. The low processing time ( < 4 s per 1280 × 960 pixel images) is suitable for batch processing of experimental data as well as for interactive segmentation applications

A Novel Representation for Two-dimensional Image Structures

This paper presents a novel approach towards two-dimensional (2D) image structures modeling. To obtain more degrees of freedom, a 2D image signal is embedded into a certain geometric algebra. Coupling methods of differential geometry, tensor algebra, monogenic signal and quadrature filter, we can design a general model for 2D structures as the monogenic extension of a curvature tensor. Based on it, a local representation for the intrinsically two-dimensional (i2D) structure is derived as the monogenic curvature signal. From it, independent features of local amplitude, phase and orientation are simultaneously extracted. Besides, a monogenic curvature scale-space can be built by applying a Poisson kernel to the monogenic curvature signal. Compared with the other related work, the remarkable advantage of our approach lies in the rotationally invariant phase evaluation of 2D structures in a multi-scale framework, which delivers access to phase-based processing in many computer vision tasks

Multi-Scale Local Fourier Phase Based Feature Learning for Single Image Super Resolution

Most of the image/video processing and computer vision applications depend on the high quality image frames. Given a low resolution input, the proposed method uses multi-scale local directional Fourier phase features to adaptively learn a regression kernel based on local covariance to estimate the high resolution image. This method uses image features to learn the local covariance from geometric similarity between low resolution image and its high resolution counterpart. For each patch in the neighbourhood, we estimate four directional variances in three different scales to adapt the interpolated pixels. We use a non parametric kernel regression to learn the characteristics of local directional edge features. The Gaussian steering kernel which has the capability to elongate, rotate and scale along the edge regions is used. The parameters of elongation, rotation and scale are estimated automatically from the image local region. We apply these weights to estimate the interpolated pixels to get the high resolution image. The experimental results show that the proposed algorithm performs better than other state of the art techniques especially at higher resolution scales. This can be applied to improve the performance of object classification system on wide area motion imagery.https://ecommons.udayton.edu/stander_posters/1200/thumbnail.jp

Efficient simulation and characterization of a head-on vortex ring collision

We simulate and analyze the head-on collision between vortex rings at $Re_{\Gamma_0} =$ 4,000. We utilize an adaptive, multi-resolution solver, based on the lattice Green's function, whose fidelity is established with integral metrics representing symmetries and discretization errors. Using the velocity gradient tensor and structural features of local streamlines, we characterize the evolution of the flow with a particular focus on its transition and turbulent decay. Transition is excited by the development of the elliptic instability, which grows during the mutual interaction of the rings as they expand radially at the collision plane. The development of antiparallel secondary vortex filaments along the circumference mediates the proliferation of small-scale turbulence. During turbulent decay, the partitioning of the velocity gradients approaches an equilibrium that is dominated by shearing and agrees well with previous results for forced isotropic turbulence. We also introduce new phase spaces for the velocity gradients that reflect the interplay between shearing and rigid rotation and highlight geometric features of local streamlines. In conjunction with our visualizations, these phase spaces suggest that, while the elliptic instability is the predominant mechanism driving the initial transition, its interplay with other mechanisms, particularly the Crow instability, becomes more important during turbulent decay. Our analysis suggests that the geometry-based phase space may be promising for identifying the effects of the elliptic instability and other mechanisms using the structure of local streamlines. Moving forward, characterizing the organization of these mechanisms within vortices and universal features of velocity gradients may aid in modeling the turbulent cascade.Comment: 34 pages, 11 figure

Computational homogenisation and solution strategies for phase-field fracture

The computational modelling of fracture not only provides a deep insight into the underlying mechanisms that trigger a fracture but also offers information on the post-fracture behaviour (e.g., residual strength) of engineering materials and structures. In this context, the phase-field model for fracture is a popular approach, due to its ability to operate on fixed meshes without the need for explicit tracking of the fracture path, and the straight-forward handling of complex fracture topology. Nevertheless, the model does have its set of computational challenges viz., non-convexity of the energy functional, variational inequality due to fracture irreversibility, and the need for extremely fine meshes to resolve the fracture zone. In the first part of this thesis, two novel methods are proposed to tackle the fracture irreversibility, (i) a micromorphic approach that results in local irreversibile evolution of the phase-field, and (ii) a slack variable approach that replaces the fracture irreversibility inequality constraint with an equivalent equality constraint. Benchmark problems are solved using a monolithic Newton-Raphson solution technique to demonstrate the efficiency of both methods.The second aspect addressed in this thesis concerns multi-scale problems. In such problems, features such as the micro-cracks are extremely small (several orders of magnitude) compared to the structure itself. Resolving these features may result in a prohibitively computationally expensive problem. In order to address this issue, a computational homogenisation framework for the phase-field fracture is developed. The framework allows the computational of macro (engineering)-scale quantities using different homogenising (averaging) approaches over a microstructure. It is demonstrated that, based on the choice of the homogenisation approaches, local and non-local macro-scale material behaviour is obtained

A Compressible Σ-Υ Two-Fluid Atomization Model with Dynamic Interface Sharpening based on Flow Topology Detection

Liquid fuel atomization is characterized by multi-scale flow features and the coexistence of different flow regimes which complicate the simulation of an atomizing spray under realistic operating conditions. The present work introduces an atomization model dealing with such multi-scale complexities. The proposed model is com-pressible, so it can capture the density variations that affect spray penetration and atomization mechanisms. It is developed within a multi-phase Eulerian-Eulerian framework that considers slip velocity effects between the phases and introduces an additional transport equation for the surface area (Σ); the latter aims to model the unre-solved sub-grid scale surface area variation. Moreover, a flow topology detection algorithm is applied in the flow field aiming to distinguish between different flow regimes; finally, the numerical algorithm applies appropriate closure relations for the interfacial source terms of the two-fluid model. The interfacial structures are also treated differently depending on the flow topology; a VOF method is applied in dense spray regions for resolving the interface fully and a non-sharp interface model is imposed in dilute spray regions, where sub-grid scale models are implemented for the modelling of relevant phenomena. The efficient coupling between the two-fluid model and the VOF method is examined via a standard interface capturing validation case of a rising bubble in a stagnant liquid. For the validation of the dynamic switching between different model formulations based on local topology and the numerical stability under the coexistence of various flow regimes, a Rayleigh-Taylor instability case is simulated and tested with the proposed model

Signal Modeling for Two-Dimensional Image Structures and Scale-Space Based Image Analysis

Model based image representation plays an important role in many computer vision tasks. Consequently, it is of high significance to model image structures with more powerful representation capabilities. In the literature, there exist bulk of researches for intensity based modeling. However, most of them suffer from the illumination variation. On the other hand, phase information, which carries most essential structural information of the original signal, has the advantage of being invariant to the brightness change. Therefore, phase based image analysis is advantageous when compared to purely intensity based approaches. This thesis aims to propose novel image representations for 2D image structures, from which useful local features can be extracted, which are useful for phase based image analysis. The first approach presents a 2D rotationally invariant quadrature filter. This model is able to handle superimposed intrinsically two-dimensional (i2D) patterns with flexible angles of intersection. Hence, it can be regarded as an extension of the structure multivector. The second approach is the monogenic curvature tensor. Coupling methods of differential geometry, tensor algebra, monogenic signal and quadrature filter, we can design a general model for 2D structures as the monogenic extension of a curvature tensor. Based on it, local representations for the intrinsically one-dimensional (i1D) and i2D structures are derived as the monogenic signal and the generalized monogenic curvature signal, respectively. From them, independent features of local amplitude, phase and orientation are simultaneously extracted. Besides, a generalized monogenic curvature scale-space can be built by applying a Poisson kernel to the monogenic curvature tensor. Compared with other related work, the remarkable advantage of our approach lies in the rotationally invariant phase evaluation of 2D structures in a multi-scale framework, which delivers access to phase-based processing in many computer vision tasks. To demonstrate the efficiency and power of the theoretic framework, some computer vision applications are presented, which include the phase based image reconstruction, detecting i2D image structures using local phase and monogenic curvature tensor for optical flow estimation

Signal processing and graph-based semi-supervised learning-based fault diagnosis for direct online induction motors

In this thesis, fault diagnosis approaches for direct online induction motors are proposed using signal processing and graph-based semi-supervised learning (GSSL). These approaches are developed using experimental data obtained in the lab for two identical 0.25 HP three-phase squirrel-cage induction motors. Various electrical and mechanical single- and multi-faults are applied to each motor during experiments. Three-phase stator currents and three-dimensional vibration signals are recorded simultaneously in each experiment. In this thesis, Power Spectral Density (PSD)-based stator current amplitude spectrum analysis and one-dimensional Complex Continuous Wavelet Transform (CWT)-based stator current time-scale spectrum analysis are employed to detect broken rotor bar (BRB) faults. An effective single- and multi-fault diagnosis approach is developed using GSSL, where discrete wavelet transform (DWT) is applied to extract features from experimental stator current and vibration data. Three GSSL algorithms (Local and global consistency (LGC), Gaussian field and harmonic functions (GFHF), and greedy-gradient max-cut (GGMC)) are adopted and compared in this study. To enable machine learning for untested motor operating conditions, mathematical equations to calculate features for untested conditions are developed using curve fitting and features obtained from experimental data of tested conditions