Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons

Digital reconstruction of neuronal cell morphology is an important step toward understanding the functionality of neuronal networks. Neurons are tree-like structures whose description depends critically on the junctions and terminations, collectively called critical points, making the correct localization and identification of these points a crucial task in the reconstruction process. Here we present a fully automatic method for the integrated detection and characterization of both types of critical points in fluorescence microscopy images of neurons. In view of the majority of our current studies, which are based on cultured neurons, we describe and evaluate the method for application to two-dimensional (2D) images. The method relies on directional filtering and angular profile analysis to extract essential features about the main streamlines at any location in an image, and employs fuzzy logic with carefully designed rules to reason about the feature values in order to make well-informed decisions about the presence of a critical point and its type. Experiments on simulated as well as real images of neurons demonstrate the detection performance of our method. A comparison with the output of two existing neuron reconstruction methods reveals that our method achieves substantially higher detection rates and could provide beneficial information to the reconstruction process

Mathematical methods for modeling the microcirculation

The microcirculation plays a major role in maintaining homeostasis in the body. Alterations or dysfunctions of the microcirculation can lead to several types of serious diseases. It is not surprising, then, that the microcirculation has been an object of intense theoretical and experimental study over the past few decades. Mathematical approaches offer a valuable method for quantifying the relationships between various mechanical, hemodynamic, and regulatory factors of the microcirculation and the pathophysiology of numerous diseases. This work provides an overview of several mathematical models that describe and investigate the many different aspects of the microcirculation, including geometry of the vascular bed, blood flow in the vascular networks, solute transport and delivery to the surrounding tissue, and vessel wall mechanics under passive and active stimuli. Representing relevant phenomena across multiple spatial scales remains a major challenge in modeling the microcirculation. Nevertheless, the depth and breadth of mathematical modeling with applications in the microcirculation is demonstrated in this work. A special emphasis is placed on models of the retinal circulation, including models that predict the influence of ocular hemodynamic alterations with the progression of ocular diseases such as glaucoma

Computational analysis of blood flow and oxygen transport in the retinal arterial network

Pathological changes in retinal microvasculature are known to be associated with systemic diseases such as hypertension and diabetes, and may result in potentially disadvantageous blood flow and impair oxygen distribution. Therefore, in order to improve our understanding of the link between systemic diseases and the retinal circulation, it is necessary to develop an approach to quantitatively determine the hemodynamic and oxygen transport parameters in the retinal vascular circulation. This thesis aims to provide more insights into the detailed hemodynamic features of the retinal arterial tree by means of non-invasive imaging and computational modelling. It covers the following two aspects: i) 3D reconstruction of the retinal arterial tree, and ii) development of an image-based computational model to predict blood flow and oxygen transport in realistic subject-specific retinal arterial trees. The latter forms the main body of the thesis. 3D reconstruction of the retinal arterial tree was performed based on retinal images acquired in vivo with a fundus camera and validated using a simple 3D object. The reproduction procedure was found to be feasible but with limited accuracy. In the proposed 2D computational model, the smaller peripheral vessels indistinguishable from the retinal images were represented by self-similar asymmetric structured trees. The non-Newtonian properties of blood, and nonlinear oxyhemoglobin dissociation in the red blood cells and plasma were considered. The simulation results of the computational model were found in good agreement with in vivo measurements reported in the literature. In order to understand the effect of retinal vascular structure on blood flow and oxygen transport, the computational model was applied to subject-specific geometries for a number of hypertensive and diabetic patients, and comparisons were made with results obtained from healthy retinal arterial networks. Moreover, energy analysis of normal and hypertensive subjects was performed using 3D hypothetical models. Finally, the influence of different viscosity models on flow and oxygen transport in a retinal tree and the advantage of low dimensional models were examined. This study has demonstrated the applicability of the image-based computational modelling to study the hemodynamics and oxygen distribution in the retinal arterial network

A retinal vasculature tracking system guided by a deep architecture

Many diseases such as diabetic retinopathy (DR) and cardiovascular diseases show their early signs on retinal vasculature. Analysing the vasculature in fundus images may provide a tool for ophthalmologists to diagnose eye-related diseases and to monitor their progression. These analyses may also facilitate the discovery of new relations between changes on retinal vasculature and the existence or progression of related diseases or to validate present relations. In this thesis, a data driven method, namely a Translational Deep Belief Net (a TDBN), is adapted to vasculature segmentation. The segmentation performance of the TDBN on low resolution images was found to be comparable to that of the best-performing methods. Later, this network is used for the implementation of super-resolution for the segmentation of high resolution images. This approach provided an acceleration during segmentation, which relates to down-sampling ratio of an input fundus image. Finally, the TDBN is extended for the generation of probability maps for the existence of vessel parts, namely vessel interior, centreline, boundary and crossing/bifurcation patterns in centrelines. These probability maps are used to guide a probabilistic vasculature tracking system. Although segmentation can provide vasculature existence in a fundus image, it does not give quantifiable measures for vasculature. The latter has more practical value in medical clinics. In the second half of the thesis, a retinal vasculature tracking system is presented. This system uses Particle Filters to describe vessel morphology and topology. Apart from previous studies, the guidance for tracking is provided with the combination of probability maps generated by the TDBN. The experiments on a publicly available dataset, REVIEW, showed that the consistency of vessel widths predicted by the proposed method was better than that obtained from observers. Moreover, very noisy and low contrast vessel boundaries, which were hardly identifiable to the naked eye, were accurately estimated by the proposed tracking system. Also, bifurcation/crossing locations during the course of tracking were detected almost completely. Considering these promising initial results, future work involves analysing the performance of the tracking system on automatic detection of complete vessel networks in fundus images.Open Acces

Human treelike tubular structure segmentation: A comprehensive review and future perspectives

Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed

CLBP for Retinal Vascular Occlusion Detection

Abstract Retinal vein occlusion (RVO) and diabetic retinal disease is the most widespread type of retinal disorders. The precisely extracted Retinal blood vessel provides a vital factor in the early diagnosis of retinopathy. In this paper a programmed method is proposed to enhance the performance evaluation of feature extraction technique to obtain the affected ocular fundus images in the retinal blood vessel database. The ophthalmologists make use of these tools for patient screening, treatment evaluation, and clinical study. The study consists of two parts: an image acquisition and an adept algorithm for performance rate calculation to detect the retinal vein occlusion in human eye. The features are extracted using completed local binary pattern and the extracted data's are classified using artificial neural network. The precise results exhibit the feasibility of the proposed system in terms of performance evaluation