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Ph.DDOCTOR OF PHILOSOPH

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Ph.DDOCTOR OF PHILOSOPH

Multiple-view microscopy with light-sheet based fluorescence microscope

The axial resolution of any standard single-lens light microscope is lower than its lateral resolution. The ratio is approximately 3-4 when high numerical aperture objective lenses are used (NA 1.2 -1.4) and more than 10 with low numerical apertures (NA 0.2 and below). In biological imaging, the axial resolution is normally insufficient to resolve subcellular phenomena. Furthermore, parts of the images of opaque specimens are often highly degraded or obscured. Multiple-view fluorescence microscopy overcomes both problems simultaneously by recording multiple images of the same specimen along different directions. The images are digitally fused into a single high-quality image. Multiple-view imaging was developed as an extension to the light-sheet based fluorescence microscope (LSFM), a novel technique that seems to be better suited for multiple-view imaging than any other fluorescence microscopy method to date. In this contribution, the LSFM properties, which are important for multiple-view imaging, are characterized and the implementation of LSFM based multiple-view microscopy is described. The important aspects of multiple-view image alignment and fusion are discussed, the published algorithms are reviewed and original solutions are proposed. The advantages and limitations of multiple-view imaging with LSFM are demonstrated using a number of specimens, which range in size from a single yeast cell to an adult fruit fly and to Medaka fish

Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light’s diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we’ve termed the interpretation problem

Towards development of automatic path planning system in image-guided neurosurgery

With the advent of advanced computer technology, many computer-aided systems have evolved to assist in medical related work including treatment, diagnosis, and even surgery. In modern neurosurgery, Magnetic Resonance Image guided stereotactic surgery exactly complies with this trend. It is a minimally invasive operation being much safer than the traditional open-skull surgery, and offers higher precision and more effective operating procedures compared to conventional craniotomy. However, such operations still face significant challenges of planning the optimal neurosurgical path in order to reach the ideal position without damage to important internal structures. This research aims to address this major challenge. The work begins with an investigation of the problem of distortion induced by MR images. It then goes on to build a template of the Circle of Wills brain vessels, realized from a collection of Magnetic Resonance Angiography images, which is needed to maintain operating standards when, as in many cases, Magnetic Resonance Angiography images are not available for patients. Demographic data of brain tumours are also studied to obtain further understanding of diseased human brains through the development of an effect classifier. The developed system allows the internal brain structure to be ‘seen’ clearly before the surgery, giving surgeons a clear picture and thereby makes a significant contribution to the eventual development of a fully automatic path planning system

Sustainable control of infestations using image processing and modelling

A sustainable pest control system integrates automated pest detection and recognition to evaluate the pest density using image samples taken from habitats. Novel predator/prey modelling algorithms assess control requirements for the UAV system, which is designed to deliver measured quantities of naturally beneficial predators to combat pest infestations within economically acceptable timeframes. The integrated system will reduce the damaging effect of pests in an infested habitat to an economically acceptable level without the use of chemical pesticides. Plant pest recognition and detection is vital for food security, quality of life and a stable agricultural economy. The research utilises a combination of the k-means clustering algorithm and the correspondence filter to achieve pest detection and recognition. The detection is achieved by partitioning the data space into Voronoi cells, which tends to find clusters of comparable spatial extents, thereby separating the objects (pests) from the background (pest habitat). The detection is established by extracting the variant and distinctive attributes between the pest and its habitat (leaf, stem) and using the correspondence filter to identify the plant pests to obtain correlation peak values for the different datasets. The correspondence filter can achieve rotationally invariant recognition of pests for a full 360 degrees, which proves the effectiveness of the algorithm and provides a count of the number of pests in the image. A series of models has been produced that will permit an assessment of common pest infestation problems and estimate the number of predators that are required to control the problem within a time schedule. A UAV predator deployment system has been designed. The system is offered as a replacement for chemical pesticides to improve peoples’ health opportunities and the quality of food products

Choran community: The aesthetics of encounter in literary and photographic modernism

This dissertation examines novels, photographs, and phototexts by British and American artists published between the world wars in order to argue that these works re-envision community through a narrative aesthetic, which I term the choran moment, that communicates the possibility of genuinely empathetic understanding between self and other. My study of literary and photographic modernism is based upon these modern artists\u27 awareness of an ever-present, organic community allied in common knowledge of the interconnection among humanity offered through convergence with and respect for difference. These choran moments of correlation are key to the aesthetics and therefore the politics of modernist writers Virginia Woolf, Sylvia Townsend Warner, Nella Larsen, and Zora Neale Hurston, and photographers Gertrude Kasebier, James Van Der Zee, and Walker Evans. The artists I discuss share a common humanist concern for creating moments of wholeness in their work. Moreover, their evocations of choran moments lead to communal interconnectivity for both artist and audience. The longing to rediscover a choran moment allows modern artists and audiences to rediscover a wholeness of self---the first step toward finding intersubjectivity and, finally, interconnective community through art. The ethical encounter, enacted in the choran moment, invites both contemporary audiences and the present scholarly community to read modernism as an attempt at rebuilding interconnectivity. Through my intervention into established critical categories of Modernism, I identify a particular expression of the period by examining how a broad selection of writers and photographers engage with a common humanist concern for recreating community through their art. My assessment of a diverse set of writers and photographers enables literary critics to include all of these previously unconnected artists under a new critical category of modernist narratives of community in order to see the work of these modernists as interconnected, resonant, and mutually productive. We are the scholars who can benefit from these artists\u27 potentially transformative aesthetic of modernist choran moments and communal interconnectivity

Dialogic Dissensus: The Postmodern Sideshow

In the United States, the sideshow occupies a marginal and often controversial space in popular culture. Despite a decline of the sideshow during the early twentieth century, its postmodern reinvention in 1980 has inspired a proliferation of the aesthetics of the sideshow within mass media and culture as a highly profitable commodity. The current existence of the sideshow as a thriving genre can sometimes be met with surprise, disbelief, or disgust because of the history of sideshow and existing codes of “normality.” Although there is pre-existing scholarship on Bakhtin and the sideshow, what is missing is an exploration of Bakhtin’s dialogism in relation to the art of the postmodern sideshow. This dissertation argues that the postmodern sideshow as an art form is an example of a reinvention of intersubjectivity through Bakhtin’s dialogic and still relevant for understanding contemporary aesthetics. Furthermore, I propose that the carnivalesque is an aspect of the dialogic because the carnivalesque renews hope for a better future which reverberates through unfinalizable time. Instead, I will propose an intertextual genealogy between philosophical thought and the first-hand voices of sideshow performers and related show people in the spirit of dialogism. However, I assert that the dialogic is nearly impossible without a dissensus because of precarization and our permanent cellular connection as a result of our technological progress, which did not exist at the height of postmodernism. This new tyranny of normality has depersonalized our time, dissolved our friendships and communities, our ability to communicate, and our social consciousness to empathize with others in a fundamental shift to our notions of exploitation. A revolution of the aesthetic regime through the maternal will create a new paradigm that reorganizes our senses, our social consciousness, and the conditions for possibility in the dialogic.https://digitalmaine.com/academic/1022/thumbnail.jp

A feature-based reverse engineering system using artificial neural networks

Reverse Engineering (RE) is the process of reconstructing CAD models from scanned data of a physical part acquired using 3D scanners. RE has attracted a great deal of research interest over the last decade. However, a review of the literature reveals that most research work have focused on creation of free form surfaces from point cloud data. Representing geometry in terms of surface patches is adequate to represent positional information, but can not capture any of the higher level structure of the part. Reconstructing solid models is of importance since the resulting solid models can be directly imported into commercial solid modellers for various manufacturing activities such as process planning, integral property computation, assembly analysis, and other applications. This research discusses the novel methodology of extracting geometric features directly from a data set of 3D scanned points, which utilises the concepts of artificial neural networks (ANNs). In order to design and develop a generic feature-based RE system for prismatic parts, the following five main tasks were investigated. (1) point data processing algorithms; (2) edge detection strategies; (3) a feature recogniser using ANNs; (4) a feature extraction module; (5) a CAD model exchanger into other CAD/CAM systems via IGES. A key feature of this research is the incorporation of ANN in feature recognition. The use of ANN approach has enabled the development of a flexible feature-based RE methodology that can be trained to deal with new features. ANNs require parallel input patterns. In this research, four geometric attributes extracted from a point set are input to the ANN module for feature recognition: chain codes, convex/concave, circular/rectangular and open/closed attribute. Recognising each feature requires the determination of these attributes. New and robust algorithms are developed for determining these attributes for each of the features. This feature-based approach currently focuses on solving the feature recognition problem based on 2.5D shapes such as block pocket, step, slot, hole, and boss, which are common and crucial in mechanical engineering products. This approach is validated using a set of industrial components. The test results show that the strategy for recognising features is reliable