Studies on the identification and characterisation of certain fish viruses with special reference to lymphocystis and piscine erythrocytic necrosis (PEN) viruses

This is a digitised version of a thesis that was deposited in the University Library. If you are the author and you have a query about this item please contact PEARL Admin ([email protected])Metadata merged with duplicate record (http://hdl.handle.net/10026.1/2302) on 20.12.2016 by CS (TIS).Studies were performed on two types of infection of teleost fish where viruses have been observed by electron microscopy: erythrocytic infections in the Atlantic Cod (Gadus morhua) and the Common Blenny (Blennius hpo lis) and lymphcystis disease., Searches were made for new, isolations of these infections Ja British coastal waters and on shores chiefly in the vicinity of Plymouth and Aberystwyth. In the absence of disease symptoms, the blood of fish was, screened for the presence of viral inclusion bodies by standard haematological methods. PEN in cod was found in the North Sea-. and in the Celtic Sea off southern Eire, thus extending the previous distribution data from the Atlantic-coastal waters. of North America. The blenny infection was also found in new sites on shores in the vicinity of Plymouth. Moreover, the cytology of these infectionswas as had been previously described. Collection data for the PEN infections showed an inverses; - relationship of infection incidence-with age for cod sample populations but no correlation was found for blenny sample populations. In addition, no external disease symptoms were observed in either type of infection. Concerning the recognition of the blenny infection, observations from maintaining blennies suggested the length of the natural infection might be inversely related to temperature; non-experimental longevities are quoted in this connection. The degree of infection in individual fish was estimated by light microscopy and the estimates for both erythrocytic infections cover the range 1-60% infection. Attempts were made to propagate the viruses in vitro using fish cell and organ cultures. Primary cell cultures were originated from tissues of the Blenny, Flounder, Plaice and Dab using the protocol in the literature for marine fish cell culture. Vigorous cell outgrowth was observed in the flounder cultures and in these the time to confluence was only 3-5 days. However, established secondary cultures could not be derived from tissues of either species. Plaice and dab cultures were used for virus inoculation but the virus from the blenny infection and lymphocystis virus could not be propagated. , Organ cultures were set up using skin blocks from the Flounder. With tris-buffered maintenance medium such cultures maintained histological integrity for 15 days. However, one - trial inoculation with lymphocystis virus showed no-integration or multiplication of the virus in the tissue. In connection with attempts to induce the blenny infection, the. effect of high temperature-in the Blenny was investigated. The infection was not induced over a9 day holding period but lytic effects on the erythrocyte nuclei were observed. The effect of the drug acetylphenylhydrazine (APH) in the Blenny was also investigated with the aim of reproducing its reported action of anaemia induction and ensuing erythropoiesis. Marked anaemia was produced but not erythropoiesis. However, this result could not nesessarily be interpreted as the effect of APH alone. The viruses were identified and characterized with emphasis on their mophology, using ultrathin sectioning, negative staining and shadowing methods. It was concluded that the virus from the Blenny and lymphocystis virus conform to the structural measurements in the literature but negative staining indicated that both viruses display unique core structures. These are discussed in the light of the knowledge of other DNA virus cores. The position of these viruses is further considered with respect to their classification in the virus family Iridoviridae.University College of Wales, Aberystwyt

Automatic Segmentation and Classification of Red and White Blood cells in Thin Blood Smear Slides

In this work we develop a system for automatic detection and classification of cytological images which plays an increasing important role in medical diagnosis. A primary aim of this work is the accurate segmentation of cytological images of blood smears and subsequent feature extraction, along with studying related classification problems such as the identification and counting of peripheral blood smear particles, and classification of white blood cell into types five. Our proposed approach benefits from powerful image processing techniques to perform complete blood count (CBC) without human intervention. The general framework in this blood smear analysis research is as follows. Firstly, a digital blood smear image is de-noised using optimized Bayesian non-local means filter to design a dependable cell counting system that may be used under different image capture conditions. Then an edge preservation technique with Kuwahara filter is used to recover degraded and blurred white blood cell boundaries in blood smear images while reducing the residual negative effect of noise in images. After denoising and edge enhancement, the next step is binarization using combination of Otsu and Niblack to separate the cells and stained background. Cells separation and counting is achieved by granulometry, advanced active contours without edges, and morphological operators with watershed algorithm. Following this is the recognition of different types of white blood cells (WBCs), and also red blood cells (RBCs) segmentation. Using three main types of features: shape, intensity, and texture invariant features in combination with a variety of classifiers is next step. The following features are used in this work: intensity histogram features, invariant moments, the relative area, co-occurrence and run-length matrices, dual tree complex wavelet transform features, Haralick and Tamura features. Next, different statistical approaches involving correlation, distribution and redundancy are used to measure of the dependency between a set of features and to select feature variables on the white blood cell classification. A global sensitivity analysis with random sampling-high dimensional model representation (RS-HDMR) which can deal with independent and dependent input feature variables is used to assess dominate discriminatory power and the reliability of feature which leads to an efficient feature selection. These feature selection results are compared in experiments with branch and bound method and with sequential forward selection (SFS), respectively. This work examines support vector machine (SVM) and Convolutional Neural Networks (LeNet5) in connection with white blood cell classification. Finally, white blood cell classification system is validated in experiments conducted on cytological images of normal poor quality blood smears. These experimental results are also assessed with ground truth manually obtained from medical experts

Added benefits of computer-assisted analysis of Hematoxylin-Eosin stained breast histopathological digital slides

This thesis aims at determining if computer-assisted analysis can be used to better understand pathologists’ perception of mitotic figures on Hematoxylin-Eosin (HE) stained breast histopathological digital slides. It also explores the feasibility of reproducible histologic nuclear atypia scoring by incorporating computer-assisted analysis to cytological scores given by a pathologist. In addition, this thesis investigates the possibility of computer-assisted diagnosis for categorizing HE breast images into different subtypes of cancer or benign masses. In the first study, a data set of 453 mitoses and 265 miscounted non-mitoses within breast cancer digital slides were considered. Different features were extracted from the objects in different channels of eight colour spaces. The findings from the first research study suggested that computer-aided image analysis can provide a better understanding of image-related features related to discrepancies among pathologists in recognition of mitoses. Two tasks done routinely by the pathologists are making diagnosis and grading the breast cancer. In the second study, a new tool for reproducible nuclear atypia scoring in breast cancer histological images was proposed. The third study proposed and tested MuDeRN (MUlti-category classification of breast histopathological image using DEep Residual Networks), which is a framework for classifying hematoxylin-eosin stained breast digital slides either as benign or cancer, and then categorizing cancer and benign cases into four different subtypes each. The studies indicated that computer-assisted analysis can aid in both nuclear grading (COMPASS) and breast cancer diagnosis (MuDeRN). The results could be used to improve current status of breast cancer prognosis estimation through reducing the inter-pathologist disagreement in counting mitotic figures and reproducible nuclear grading. It can also improve providing a second opinion to the pathologist for making a diagnosis

FTIR imaging: a route toward automated histopathology

The focus of this study is the potential use of FTIR imaging as a tool for objective automated histopathology. The Thesis also reports the use of multivariate statistical techniques to analyse the FTIR imaging data. These include Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Multivariate Curve Resolution (MCR) and Fuzzy C-Means Clustering (FCM). The development of a new PCA-FCM Clustering hybrid that can automatically detect the optimum clustering structure is also reported. Chapter 1 provides a brief introduction to the use of vibrational spectroscopy to characterise biomolecules in tissues and cells for medical diagnosis. Chapter 2 details the basic histology of a lymph node before proceeding to present imaging results gained from the analysis of both healthy and diseased lymph node tissue sections. The ability of each multivariate technique to discriminate different tissue types is discussed. In addition, the spectral features that are characteristic for each tissue type are reported. The development and application of a new PCA-FCM Clustering algorithm that can automatically determine the best clustering structure is also described in full. The results indicate that cellular abnormality provides changes to both the protein and nucleic acid vibrations. However, similar spectral profiles were identified for highly proliferating cells that were contained within reactive germinal centres of the lymph node. Chapter 3 provides a short introduction to the histology of the cervlx before presenting imaging results that were gained from the analysis of both healthy and diseased cervical tissue sections. The ability of each multivariate technique to discriminate different tissue types is discussed. In addition, the spectral features that are characteristic for each tissue type are described in detail. Novel imaging experiments upon exfoliated cervical cells are also presented. It would appear that cellular abnormality in cervical tissues and cells affects both the protein and nucleic acid features of the spectra. Glycogen and glycoprotein contributions that are prevalent in healthy tissues are also absent. Chapter 4 details sample preparation methods, the instrumentation and procedures used for data acquisition, and the subsequent data processing and multivariate techniques applied to analyse the collected spectral datasets

Computer aided diagnosis algorithms for digital microscopy

Automatic analysis and information extraction from an image is still a highly chal- lenging research problem in the computer vision area, attempting to describe the image content with computational and mathematical techniques. Moreover the in- formation extracted from the image should be meaningful and as most discrimi- natory as possible, since it will be used to categorize its content according to the analysed problem. In the Medical Imaging domain this issue is even more felt because many important decisions that affect the patient care, depend on the use- fulness of the information extracted from the image. Manage medical image is even more complicated not only due to the importance of the problem, but also because it needs a fair amount of prior medical knowledge to be able to represent with data the visual information to which pathologist refer. Today medical decisions that impact patient care rely on the results of laboratory tests to a greater extent than ever before, due to the marked expansion in the number and complexity of offered tests. These developments promise to improve the care of patients, but the more increase the number and complexity of the tests, the more increases the possibility to misapply and misinterpret the test themselves, leading to inappropriate diagnosis and therapies. Moreover, with the increased number of tests also the amount of data to be analysed increases, forcing pathologists to devote much time to the analysis of the tests themselves rather than to patient care and the prescription of the right therapy, especially considering that most of the tests performed are just check up tests and most of the analysed samples come from healthy patients. Then, a quantitative evaluation of medical images is really essential to overcome uncertainty and subjectivity, but also to greatly reduce the amount of data and the timing for the analysis. In the last few years, many computer assisted diagno- sis systems have been developed, attempting to mimic pathologists by extracting features from the images. Image analysis involves complex algorithms to identify and characterize cells or tissues using image pattern recognition technology. This thesis addresses the main problems associated to the digital microscopy analysis in histology and haematology diagnosis, with the development of algorithms for the extraction of useful information from different digital images, but able to distinguish different biological structures in the images themselves. The proposed methods not only aim to improve the degree of accuracy of the analysis, and reducing time, if used as the only means of diagnoses, but also they can be used as intermediate tools for skimming the number of samples to be analysed directly from the pathologist, or as double check systems to verify the correct results of the automated facilities used today

Computer aided diagnosis algorithms for digital microscopy

Automatic analysis and information extraction from an image is still a highly chal- lenging research problem in the computer vision area, attempting to describe the image content with computational and mathematical techniques. Moreover the in- formation extracted from the image should be meaningful and as most discrimi- natory as possible, since it will be used to categorize its content according to the analysed problem. In the Medical Imaging domain this issue is even more felt because many important decisions that affect the patient care, depend on the use- fulness of the information extracted from the image. Manage medical image is even more complicated not only due to the importance of the problem, but also because it needs a fair amount of prior medical knowledge to be able to represent with data the visual information to which pathologist refer. Today medical decisions that impact patient care rely on the results of laboratory tests to a greater extent than ever before, due to the marked expansion in the number and complexity of offered tests. These developments promise to improve the care of patients, but the more increase the number and complexity of the tests, the more increases the possibility to misapply and misinterpret the test themselves, leading to inappropriate diagnosis and therapies. Moreover, with the increased number of tests also the amount of data to be analysed increases, forcing pathologists to devote much time to the analysis of the tests themselves rather than to patient care and the prescription of the right therapy, especially considering that most of the tests performed are just check up tests and most of the analysed samples come from healthy patients. Then, a quantitative evaluation of medical images is really essential to overcome uncertainty and subjectivity, but also to greatly reduce the amount of data and the timing for the analysis. In the last few years, many computer assisted diagno- sis systems have been developed, attempting to mimic pathologists by extracting features from the images. Image analysis involves complex algorithms to identify and characterize cells or tissues using image pattern recognition technology. This thesis addresses the main problems associated to the digital microscopy analysis in histology and haematology diagnosis, with the development of algorithms for the extraction of useful information from different digital images, but able to distinguish different biological structures in the images themselves. The proposed methods not only aim to improve the degree of accuracy of the analysis, and reducing time, if used as the only means of diagnoses, but also they can be used as intermediate tools for skimming the number of samples to be analysed directly from the pathologist, or as double check systems to verify the correct results of the automated facilities used today

FTIR imaging: a route toward automated histopathology

The focus of this study is the potential use of FTIR imaging as a tool for objective automated histopathology. The Thesis also reports the use of multivariate statistical techniques to analyse the FTIR imaging data. These include Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Multivariate Curve Resolution (MCR) and Fuzzy C-Means Clustering (FCM). The development of a new PCA-FCM Clustering hybrid that can automatically detect the optimum clustering structure is also reported. Chapter 1 provides a brief introduction to the use of vibrational spectroscopy to characterise biomolecules in tissues and cells for medical diagnosis. Chapter 2 details the basic histology of a lymph node before proceeding to present imaging results gained from the analysis of both healthy and diseased lymph node tissue sections. The ability of each multivariate technique to discriminate different tissue types is discussed. In addition, the spectral features that are characteristic for each tissue type are reported. The development and application of a new PCA-FCM Clustering algorithm that can automatically determine the best clustering structure is also described in full. The results indicate that cellular abnormality provides changes to both the protein and nucleic acid vibrations. However, similar spectral profiles were identified for highly proliferating cells that were contained within reactive germinal centres of the lymph node. Chapter 3 provides a short introduction to the histology of the cervlx before presenting imaging results that were gained from the analysis of both healthy and diseased cervical tissue sections. The ability of each multivariate technique to discriminate different tissue types is discussed. In addition, the spectral features that are characteristic for each tissue type are described in detail. Novel imaging experiments upon exfoliated cervical cells are also presented. It would appear that cellular abnormality in cervical tissues and cells affects both the protein and nucleic acid features of the spectra. Glycogen and glycoprotein contributions that are prevalent in healthy tissues are also absent. Chapter 4 details sample preparation methods, the instrumentation and procedures used for data acquisition, and the subsequent data processing and multivariate techniques applied to analyse the collected spectral datasets