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

The role of invariant line junctions in object and visual word recognition

AbstractObject recognition relies heavily on invariant visual features such as the manner in which lines meet at vertices to form viewpoint-invariant junctions (e.g. T, L). We wondered whether these features also underlie readers’ competence for fast recognition of printed words. Since reading is far too recent to have exerted any evolutionary pressure on brain evolution, visual word recognition might be based on pre-existing mechanisms common to all visual object recognition. In a naming task, we presented partially deleted pictures of objects and printed words in which either the vertices or the line midsegments were preserved. Subjects showed an identical pattern of behavior with both objects and words: they made fewer errors and were faster to respond when vertices were preserved. Our results suggest that vertex invariants are used for object recognition and that this evolutionarily ancient mechanism is being co-opted for reading

Stereoscopic Surface Interpolation from Illusory Contours

Stereoscopic Kanizsa figures are an example of stereoscopic interpolation of an illusory surface. In such stimuli, luminance-defined disparity signals exist only along the edges of inducing elements, but observers reliably perceive a coherent surface that extends across the central region in depth. The aim of this series of experiments was to understand the nature of the disparity signal that underlies the perception of illusory stereoscopic surfaces. I systematically assessed the accuracy and precision of suprathreshold depth percepts using a collection of Kanizsa figures with a wide range of 2D and 3D properties. For comparison, I assessed similar perceptually equated figures with luminance-defined surfaces, with and without inducing elements. A cue combination analysis revealed that observers rely on ordinal depth cues in conjunction with stereopsis when making depth judgements. Thus, 2D properties (e.g. occlusion features and luminance relationships) contribute rich information about 3D surface structure by influencing perceived depth from binocular disparity

Reconstructing Curvilinear Networks using Path Classifiers and Integer Programming

We propose a novel Bayesian approach to automated delineation of curvilinear structures that form complex and potentially loopy networks. By representing the image data as a graph of potential paths, we first show how to weight these paths using discriminatively-trained classifiers that are both robust and generic enough to be applied to very different imaging modalities. We then present an Integer Programming approach to finding the optimal subset of paths, subject to structural and topological constraints that eliminate implausible solutions. Unlike earlier approaches that assume a tree topology for the networks, ours explicitly models the fact that the networks may contain loops, and can reconstruct both cyclic and acyclic ones. We demonstrate the effectiveness of our approach on a variety of challenging datasets including aerial images of road networks and micrographs of neural arbors, and show that it outperforms state-of-the-art techniques

Methods for Automated Neuron Image Analysis

Knowledge of neuronal cell morphology is essential for performing specialized analyses in the endeavor to understand neuron behavior and unravel the underlying principles of brain function. Neurons can be captured with a high level of detail using modern microscopes, but many neuroscientific studies require a more explicit and accessible representation than offered by the resulting images, underscoring the need for digital reconstruction of neuronal morphology from the images into a tree-like graph structure. This thesis proposes new computational methods for automated detection and reconstruction of neurons from fluorescence microscopy images. Specifically, the successive chapters describe and evaluate original solutions to problems such as the detection of landmarks (critical points) of the neuronal tree, complete tracing and reconstruction of the tree, and the detection of regions containing neurons in high-content screens

Characteristics of Supraglacial Channels and Drainage Networks on Antarctic Ice Shelves

Supraglacial channels that flow on ice shelves can store and transport large volumes of meltwater to various locations (e.g., moulins, lakes, crevasses) during the melt season, so they play an important role in glacial hydrology and ice shelf stability. However, the current understanding of supraglacial channels is limited, especially the underlying processes and the controls on their development and variability. This study uses multiple remotely sensed data including satellite imagery and Digital Elevation Models (DEMs) to measure supraglacial channels in Antarctica. Five contrasting ice shelves around the margin of the Antarctic Ice Sheet are chosen as the study sites – Bach, Nansen, Nivlisen, Riiser-Larsen and Roi Baudouin ice shelves. Supraglacial lakes and channels are mapped by automatic delineation method during the melt season in 2020 and 2022, and key fluvial metrics are calculated, e.g., number, length, width, depth, sinuosity, bifurcation ratio, orientation, slopes and drainage density. Extensive supraglacial lakes and channels were observed on all five Antarctic ice shelves during the peak of the melt season and most were interconnected to form a total of 119 channel networks at different scales. The results demonstrate that: (ⅰ) supraglacial channel networks often occurred in areas with low elevations and near grounding lines, (ⅱ) supraglacial channel networks on different ice shelves exhibited different drainage patterns and hydromorphic characteristics, (ⅲ) the surface topography and structural glaciology of ice shelves affected the distribution of the supraglacial channel network. Future work could focus on long-term observation of supraglacial channels and exploring the applicability of terrestrial river-related research methods (e.g., hydrological modelling) to supraglacial channels

AutoGraff: towards a computational understanding of graffiti writing and related art forms.

The aim of this thesis is to develop a system that generates letters and pictures with a style that is immediately recognizable as graffiti art or calligraphy. The proposed system can be used similarly to, and in tight integration with, conventional computer-aided geometric design tools and can be used to generate synthetic graffiti content for urban environments in games and in movies, and to guide robotic or fabrication systems that can materialise the output of the system with physical drawing media. The thesis is divided into two main parts. The first part describes a set of stroke primitives, building blocks that can be combined to generate different designs that resemble graffiti or calligraphy. These primitives mimic the process typically used to design graffiti letters and exploit well known principles of motor control to model the way in which an artist moves when incrementally tracing stylised letter forms. The second part demonstrates how these stroke primitives can be automatically recovered from input geometry defined in vector form, such as the digitised traces of writing made by a user, or the glyph outlines in a font. This procedure converts the input geometry into a seed that can be transformed into a variety of calligraphic and graffiti stylisations, which depend on parametric variations of the strokes

Neural models of inter-cortical networks in the primate visual system for navigation, attention, path perception, and static and kinetic figure-ground perception

Vision provides the primary means by which many animals distinguish foreground objects from their background and coordinate locomotion through complex environments. The present thesis focuses on mechanisms within the visual system that afford figure-ground segregation and self-motion perception. These processes are modeled as emergent outcomes of dynamical interactions among neural populations in several brain areas. This dissertation specifies and simulates how border-ownership signals emerge in cortex, and how the medial superior temporal area (MSTd) represents path of travel and heading, in the presence of independently moving objects (IMOs). Neurons in visual cortex that signal border-ownership, the perception that a border belongs to a figure and not its background, have been identified but the underlying mechanisms have been unclear. A model is presented that demonstrates that inter-areal interactions across model visual areas V1-V2-V4 afford border-ownership signals similar to those reported in electrophysiology for visual displays containing figures defined by luminance contrast. Competition between model neurons with different receptive field sizes is crucial for reconciling the occlusion of one object by another. The model is extended to determine border-ownership when object borders are kinetically-defined, and to detect the location and size of shapes, despite the curvature of their boundary contours. Navigation in the real world requires humans to travel along curved paths. Many perceptual models have been proposed that focus on heading, which specifies the direction of travel along straight paths, but not on path curvature. In primates, MSTd has been implicated in heading perception. A model of V1, medial temporal area (MT), and MSTd is developed herein that demonstrates how MSTd neurons can simultaneously encode path curvature and heading. Human judgments of heading are accurate in rigid environments, but are biased in the presence of IMOs. The model presented here explains the bias through recurrent connectivity in MSTd and avoids the use of differential motion detectors which, although used in existing models to discount the motion of an IMO relative to its background, is not biologically plausible. Reported modulation of the MSTd population due to attention is explained through competitive dynamics between subpopulations responding to bottom-up and top- down signals

Studies of Branched Dialkyldithiophosphinic Acids on Gold and Stretchable Gold Films Using Silica Nanoparticles

This first half of the thesis reports the synthesis of new branched symmetric dithiophosphinic acids (DTPAs), and the formation and characterization of DTPA SAMs on As-Dep and TS gold. Chapter 1 introduces the definition of self-assembled monolayers (SAMs) and SAMs with multidentate adsorbates. The binding of DTPA SAMs on gold depends on the roughness of gold: on as-deposited (As-Dep) gold, DTPA adsorbates bind in a mixture of bidentate (60%) and monodentate (40%), while on templated-stripped (TS) gold, the SAMs chelated to the gold surface. Chapter 3 investigates packing density, organization of alkyl chain and chain crystallinity of (C6C2)2DTPA and (C5C1)2DTPA SAMs on As-Dep and TS gold. The second half of the thesis focused on the research area of stretchable electronics. Chapter 2 presents the background and application of stretchable electronics and different ways that have been used to increase the stretchability and conductivity of stretchable electronics, such as conductive materials, soft substrates and topographies of substrates. Chapter 4 reports the using of E-Beam evaporation to deposit Au thin film onto polydimethyl siloxane (PDMS) elastomeric substrates, by inducing micro-structured modified fumed silica interlayer to enhance the stretchability and conductivity of the thin metal films. Also, by simply altering the weight ratio of modified silica:PDMS interlayer, the resistance change can be tuned, which leads to different functional samples