Plasmodium life cycle stage classification based quantification of malaria parasitaemia in thin blood smears.

Visual inspection for the quantification of malaria parasitaemiain (MP) and classification of life cycle stage are hard and time taking. Even though, automated techniques for the quantification of MP and their classification are reported in the literature. However, either reported techniques are imperfect or cannot deal with special issues such as anemia and hemoglobinopathies due to clumps of red blood cells (RBCs). The focus of the current work is to examine the thin blood smear microscopic images stained with Giemsa by digital image processing techniques, grading MP on independent factors (RBCs morphology) and classification of its life cycle stage. For the classification of the life cycle of malaria parasite the k-nearest neighbor, Naïve Bayes and multi-class support vector machine are employed for classification based on histograms of oriented gradients and local binary pattern features. The proposed methodology is based on inductive technique, segment malaria parasites through the adaptive machine learning techniques. The quantification accuracy of RBCs is enhanced; RBCs clumps are split by analysis of concavity regions for focal points. Further, classification of infected and non-infected RBCs has been made to grade MP precisely. The training and testing of the proposed approach on benchmark dataset with respect to ground truth data, yield 96.75% MP sensitivity and 94.59% specificity. Additionally, the proposed approach addresses the process with independent factors (RBCs morphology). Finally, it is an economical solution for MP grading in immense testing

Discrete Element Modeling of the Grading- and Shape-Dependent Behavior of Granular Materials

Granular materials, such as sand, biomass particles, and pharmaceutical pills, are widespread in nature, industrial systems, and our daily life. Fundamentally, the bulk mechanical behavior of such materials is governed by the physical and morphological features of and the interactions among constituent particles at the microscopic scale. From a modeling standpoint, the particle-based discrete element method (DEM) has emerged as the most prevalent numerical tool to model and study the behavior of granular materials and the systems they form. A critical step towards an accurate and predictive DEM model is to incorporate those physical and morphological features (e.g., particle size, shape, and deformability) pertaining to the constituent particles. The main objective of this dissertation is to approach an accurate characterization and modeling of the grading- and shape-dependent behavior of granular materials by developing DEM models that incorporate realistic physical and morphological features of granular particles. Revolving around this objective, three studies are presented: image-based particle reconstruction and morphology characterization, grading and shape-dependent shearing behavior of rigid-particle systems, and granular flow of deformable irregular particles. The first study presents a machine learning and level-set based framework to re- construct granular particles and to characterize particle morphology from X-ray computed tomography (X-ray CT) imaging of realistic granular materials. Images containing detailed microstructure information of a granular material are obtained using the X-ray CT tech- nique. Approaches such as the watershed method in two dimensions (2D) and the combined machine learning and level set method in three dimensions (3D) are then utilized and implemented to segment X-ray CT images and to numerically reconstruct individual particles in the granular material. Based on the realistic particle shapes, particle morphology is characterized by descriptors including aspect ratio, roundness, circularity (2D) or sphericity (3D). The particle shapes or morphology provide important constraints to develop DEM models with particle physical and morphological features conforming to the specific granular material of interest. In the second study, DEM models incorporated with realistic particle sizes and shapes are developed and applied to study the shearing behavior of sandy soils. The particle sizes and shapes are obtained from realistic samples of JSC-1A Martian regolith simulant. Irregular-shape particles are represented by rigid clumps based on the domain overlapping filling method. The effects of particle shape irregularity on the shearing behavior of granular materials are investigated through direct shear tests, along with the comparisons from spherical particles with or without rolling resistance. The micro-mechanisms of shape irregularity contributing to the shear resistance are identified. The last study investigates the effects of particle deformability (e.g., compression, deflection or torsion), together with particle sizes and shapes, on the granular flow of flexible granular materials. A bonded-sphere DEM model is implemented with the capability of embodying various particle sizes and irregular shapes, as well as capturing particle deformability. This approach is then applied to simulate and study the behavior of flexible granular materials in cyclic compression and hopper flow tests. The effects of particle size, shape and deformability on the bulk mechanical behavior are investigated on the basis of the DEM simulation results. The importance of particle deformability to the DEM simulations of flexible granular materials is demonstrated

Characterising the Multi-Scale Properties of Flocculated Sediment by X-ray and Focused Ion Beam Nano-Tomography

PhDThe hydrodynamic behaviour of fine suspended aqueous sediments, and stability of the bedforms they create once settled, are governed by the physical properties (e.g., size, shape, porosity and density) of the flocculated particles in suspension (flocs). Consequently, accurate prediction of the transport and fate of sediments and of the nutrients and pollutants they carry depends on our ability to characterise aqueous flocs. Current research primarily focuses on characterising flocs based on their external gross-scale (>1 μm) properties (e.g., gross morphology, size and settling velocity) using in situ techniques such as photography and videography. Whilst these techniques provide valuable information regarding the outward behaviour of flocculated sediment (i.e. transport and settling), difficulties associated with extracting 3D geometries from 2D projections raises concerns regarding their accuracy and key parameters such as density can only be estimated. In addition, they neglect to inform on the internal micro- and nano-scale structure of flocs, responsible for much of their behaviour and development. Transmission electron microscope (TEM) and environmental electron microscope may be used to obtain nano-scale information in, essentially, 2D but there is a large scale gap between this information and the macro-scale of optical techniques. To address this issue this study uses 3D tomographic imaging over a range of spatial scales. Whilst commonly used in materials science and the life sciences, correlative tomography has yet to be applied in the environmental sciences. Threading together 3D Xray micro-computed tomography (X-ray μCT) and focused ion beam nano-tomography (FIBnt) with 2D TEM makes material characterisation from the centimetre to nanometre-scale possible. Here, this correlative imaging strategy is combined with a non-destructive stabilisation procedure and applied to the investigation of flocculated estuarine sediment, enabling the multi length-scale properties of flocs to be accurately described for the first time. This work has demonstrated that delicate aqueous flocs can be successfully stabilised via a resin embedding process and contrasted for both electron microscopy and X-ray tomography imaging. The 3D information obtained can be correlated across all length-scales from nm to mm revealing new information about the structure and morphology of flocs. A new system of characterising floc structure can be defined based on the association of particles and their stability in the structure rather than simply their size. This new model refutes the postulate that floc structures are fractal in nature.Engineering and Physical Sciences Research Council (EPSRC) Queen Mary University London (through the Post Graduate Research Fund) Environment Canad

Image analysis and statistical modeling for applications in cytometry and bioprocess control

Today, signal processing has a central role in many of the advancements in systems biology. Modern signal processing is required to provide efficient computational solutions to unravel complex problems that are either arduous or impossible to obtain using conventional approaches. For example, imaging-based high-throughput experiments enable cells to be examined at even subcellular level yielding huge amount of image data. Cytometry is an integral part of such experiments and involves measurement of different cell parameters which requires extraction of quantitative experimental values from cell microscopy images. In order to do that for such large number of images, fast and accurate automated image analysis methods are required. In another example, modeling of bioprocesses and their scale-up is a challenging task where different scales have different parameters and often there are more variables than the available number of observations thus requiring special methodology. In many biomedical cell microscopy studies, it is necessary to analyze the images at single cell or even subcellular level since owing to the heterogeneity of cell populations the population-averaged measurements are often inconclusive. Moreover, the emergence of imaging-based high-content screening experiments, especially for drug design, has put single cell analysis at the forefront since it is required to study the dynamics of single-cell gene expressions for tracking and quantification of cell phenotypic variations. The ability to perform single cell analysis depends on the accuracy of image segmentation in detecting individual cells from images. However, clumping of cells at both nuclei and cytoplasm level hinders accurate cell image segmentation. Part of this thesis work concentrates on developing accurate automated methods for segmentation of bright field as well as multichannel fluorescence microscopy images of cells with an emphasis on clump splitting so that cells are separated from each other as well as from background. The complexity in bioprocess development and control crave for the usage of computational modeling and data analysis approaches for process optimization and scale-up. This is also asserted by the fact that obtaining a priori knowledge needed for the development of traditional scale-up criteria may at times be difficult. Moreover, employment of efficient process modeling may provide the added advantage of automatic identification of influential control parameters. Determination of the values of the identified parameters and the ability to predict them at different scales help in process control and in achieving their scale-up. Bioprocess modeling and control can also benefit from single cell analysis where the latter could add a new dimension to the former once imaging-based in-line sensors allow for monitoring of key variables governing the processes. In this thesis we exploited signal processing techniques for statistical modeling of bioprocess and its scale-up as well as for development of fully automated methods for biomedical cell microscopy image segmentation beginning from image pre-processing and initial segmentation to clump splitting and image post-processing with the goal to facilitate the high-throughput analysis. In order to highlight the contribution of this work, we present three application case studies where we applied the developed methods to solve the problems of cell image segmentation and bioprocess modeling and scale-up