Colour coded

This 300 word publication to be published by the Society of Dyers and Colourists (SDC) is a collection of the best papers from a 4-year European project that has considered colour from the perspective of both the arts and sciences.The notion of art and science and the crossovers between the two resulted in application and funding for cross disciplinary research to host a series of training events between 2006 and 2010 Marie Curie Conferences & Training Courses (SCF) Call Identifier: FP6-Mobility-4, Euros 532,363.80 CREATE – Colour Research for European Advanced Technology Employment. The research crossovers between the fields of art, science and technology was also a subject that was initiated through Bristol’s Festival if Ideas events in May 2009. The author coordinated and chaired an event during which the C.P Snow lecture “On Two Cultures’ (1959) was re-presented by Actor Simon Cook and then a lecture made by Raymond Tallis on the notion of the Polymath. The CREATE project has a worldwide impact for researchers, academics and scientists. Between January and October 2009, the site has received 221, 414 visits. The most popular route into the site is via the welcome page. The main groups of visitors originate in the UK (including Northern Ireland), Italy, France, Finland, Norway, Hungary, USA, Finland and Spain. A basic percentage breakdown of the traffic over ten months indicates: USA -15%; UK - 16%; Italy - 13%; France -12%; Hungary - 10%; Spain - 6%; Finland - 9%; Norway - 5%. The remaining approximate 14% of visitors are from other countries including Belgium, The Netherlands and Germany (approx 3%). A discussion group has been initiated by the author as part of the CREATE project to facilitate an ongoing dialogue between artists and scientists. http://createcolour.ning.com/group/artandscience www.create.uwe.ac.uk.Related papers to this research: A report on the CREATE Italian event: Colour in cultural heritage.C. Parraman, A. Rizzi, ‘Developing the CREATE network in Europe’, in Colour in Art, Design and Nature, Edinburgh, 24 October 2008.C. Parraman, “Mixing and describing colour”. CREATE (Training event 1), France, 2008

Whole-Brain Analysis of Cells and Circuits by Tissue Clearing and Light-Sheet Microscopy

In this photo essay, we present a sampling of technologies from laboratories at the forefront of whole-brain clearing and imaging for high-resolution analysis of cell populations and neuronal circuits. The data presented here were provided for the eponymous Mini-Symposium presented at the Society for Neuroscience's 2018 annual meeting

Automating the Reconstruction of Neuron Morphological Models: the Rivulet Algorithm Suite

The automatic reconstruction of single neuron cells is essential to enable large-scale data-driven investigations in computational neuroscience. The problem remains an open challenge due to various imaging artefacts that are caused by the fundamental limits of light microscopic imaging. Few previous methods were able to generate satisfactory neuron reconstruction models automatically without human intervention. The manual tracing of neuron models is labour heavy and time-consuming, making the collection of large-scale neuron morphology database one of the major bottlenecks in morphological neuroscience. This thesis presents a suite of algorithms that are developed to target the challenge of automatically reconstructing neuron morphological models with minimum human intervention. We first propose the Rivulet algorithm that iteratively backtracks the neuron fibres from the termini points back to the soma centre. By refining many details of the Rivulet algorithm, we later propose the Rivulet2 algorithm which not only eliminates a few hyper-parameters but also improves the robustness against noisy images. A soma surface reconstruction method was also proposed to make the neuron models biologically plausible around the soma body. The tracing algorithms, including Rivulet and Rivulet2, normally need one or more hyper-parameters for segmenting the neuron body out of the noisy background. To make this pipeline fully automatic, we propose to use 2.5D neural network to train a model to enhance the curvilinear structures of the neuron fibres. The trained neural networks can quickly highlight the fibres of interests and suppress the noise points in the background for the neuron tracing algorithms. We evaluated the proposed methods in the data released by both the DIADEM and the BigNeuron challenge. The experimental results show that our proposed tracing algorithms achieve the state-of-the-art results

Space adaptive and hierarchical Bayesian variational models for image restoration

The main contribution of this thesis is the proposal of novel space-variant regularization or penalty terms motivated by a strong statistical rational. In light of the connection between the classical variational framework and the Bayesian formulation, we will focus on the design of highly flexible priors characterized by a large number of unknown parameters. The latter will be automatically estimated by setting up a hierarchical modeling framework, i.e. introducing informative or non-informative hyperpriors depending on the information at hand on the parameters. More specifically, in the first part of the thesis we will focus on the restoration of natural images, by introducing highly parametrized distribution to model the local behavior of the gradients in the image. The resulting regularizers hold the potential to adapt to the local smoothness, directionality and sparsity in the data. The estimation of the unknown parameters will be addressed by means of non-informative hyperpriors, namely uniform distributions over the parameter domain, thus leading to the classical Maximum Likelihood approach. In the second part of the thesis, we will address the problem of designing suitable penalty terms for the recovery of sparse signals. The space-variance in the proposed penalties, corresponding to a family of informative hyperpriors, namely generalized gamma hyperpriors, will follow directly from the assumption of the independence of the components in the signal. The study of the properties of the resulting energy functionals will thus lead to the introduction of two hybrid algorithms, aimed at combining the strong sparsity promotion characterizing non-convex penalty terms with the desirable guarantees of convex optimization

Image Restoration

This book represents a sample of recent contributions of researchers all around the world in the field of image restoration. The book consists of 15 chapters organized in three main sections (Theory, Applications, Interdisciplinarity). Topics cover some different aspects of the theory of image restoration, but this book is also an occasion to highlight some new topics of research related to the emergence of some original imaging devices. From this arise some real challenging problems related to image reconstruction/restoration that open the way to some new fundamental scientific questions closely related with the world we interact with

Phase control and measurement in digital microscopy

The ongoing merger of the digital and optical components of the modern microscope is creating opportunities for new measurement techniques, along with new challenges for optical modelling. This thesis investigates several such opportunities and challenges which are particularly relevant to biomedical imaging. Fourier optics is used throughout the thesis as the underlying conceptual model, with a particular emphasis on three--dimensional Fourier optics. A new challenge for optical modelling provided by digital microscopy is the relaxation of traditional symmetry constraints on optical design. An extension of optical transfer function theory to deal with arbitrary lens pupil functions is presented in this thesis. This is used to chart the 3D vectorial structure of the spatial frequency spectrum of the intensity in the focal region of a high aperture lens when illuminated by linearly polarised beam. Wavefront coding has been used successfully in paraxial imaging systems to extend the depth of field. This is achieved by controlling the pupil phase with a cubic phase mask, and thereby balancing optical behaviour with digital processing. In this thesis I present a high aperture vectorial model for focusing with a cubic phase mask, and compare it with results calculated using the paraxial approximation. The effect of a refractive index change is also explored. High aperture measurements of the point spread function are reported, along with experimental confirmation of high aperture extended depth of field imaging of a biological specimen. Differential interference contrast is a popular method for imaging phase changes in otherwise transparent biological specimens. In this thesis I report on a new isotropic algorithm for retrieving the phase from differential interference contrast images of the phase gradient, using phase shifting, two directions of shear, and non--iterative Fourier phase integration incorporating a modified spiral phase transform. This method does not assume that the specimen has a constant amplitude. A simulation is presented which demonstrates good agreement between the retrieved phase and the phase of the simulated object, with excellent immunity to imaging noise