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38029 results in Cambridge Textbooks

Page 1696 of 1902

  • Commentary

  • Augustus, Alison E. Cooley, University of Warwick
  • Book:
    <I>Res Gestae Divi Augusti</I>
    Published online:
    28 May 2018
    Print publication:
    14 May 2009, pp 102-278

  • Summary

    HEADING

    With this Latin heading, we perhaps start with an implicit statement that Augustus' deeds are worthy both of being recorded in history and of being celebrated in the highest form of poetry, epic. This might be indicated if the way in which the words rerum gestarum divi Augusti quibus orbem form a solemn spondaic hexameter verse is not purely coincidental (Koster (1978) 242; Hoeing (1908) 90; see 1.1n. exercitum privato consilio ; 27.1n. Aegyptum ).

    The heading at Ancyra is in much larger lettering (8–4 cm) than the rest of the inscription (2 cm), and extends over the first three columns of the text. At Antioch, the heading extends above the first two columns. The original inscription at Rome must also have had a similar heading (perhaps, taking our cue from Suetonius, index rerum gestarum divi Augusti quibus orbem terrarum imperio populi Romani subiecit et impensarum quas in rem publicam populumque Romanum fecit, ‘summary of the achievements of the deified Augustus, by which he made the world subject to the rule of the Roman people, and of the expenses which he incurred for the state and people of Rome’), since otherwise the start of the inscription, annos undeviginti natus (‘Aged nineteen years old’), is too abrupt (Koster (1978) 246). The grandeur of the language suggests that the heading was composed at Rome, even though not by Augustus himself.

  • Preface

  • Augustus, Alison E. Cooley, University of Warwick
  • Book:
    <I>Res Gestae Divi Augusti</I>
    Published online:
    28 May 2018
    Print publication:
    14 May 2009, pp xi-xi

  • Summary

    I'm glad to be able to take this opportunity to thank Graham Oliver, whose enthusiastic and congenial collaboration over a paper for the Triennial Conference on the RGDA initially inspired the idea of undertaking this project. I'd also like to acknowledge the role played by my students at Warwick, notably the three cohorts who have grappled the Augustan age with me, who often make me clarify my ideas, and generally help inspire me by their enthusiasm for the subject. Michael Sharp has been supportive of the book from start to finish, offering invaluable practical help. Three Cambridge University Press assessors made helpful suggestions in planning the shape of the book in its infancy; Stephen Mitchell and the other Cambridge University Press reader provided copious suggestions for guiding the original typescript towards maturity; I hope that they like what they find now. Any faults in the book remain the result of my oversight or stubbornness. For help in compiling the illustrations I'm indebted to Michael Sharp, Stephen Mitchell, and my father. I'm grateful to Richard Abdy of the British Museum for permission to reproduce some of the images of coins already to be found in the LACTOR sourcebook, The Age of Augustus, ed. M. G. L. Cooley. Family support has been crucial, and I'm incredibly fortunate to have such tolerant children, husband, parents, and mother-in-law. Among other things, I'd single out the constructive criticism and practical help from Melvin and my parents, and the innumerable hours of childcare undertaken by my mother-in-law.

  • 14 - Frontiers of image processing in medicine

  • from Part IV - Medical applications and ongoing developments
  • Geoff Dougherty
  • Book:
    Digital Image Processing for Medical Applications
    Published online:
    12 September 2018
    Print publication:
    09 April 2009, pp 395-398

  • Summary

    Overview

    The recent rapid advances in medical imaging and automated image analysis will continue and allow us to make significant advances in our understanding of life and disease processes, and our ability to deliver quality healthcare. A few of the synergistic developments involving a number of disciplines are highlighted.

    Learning objectives

    After reading this chapter you will be able to:

  • • recognize the limitations of current imaging technology;

  • • appreciate the trends and ongoing developments in medical imaging.

    • Trends

    “A picture is worth a thousand words.”

    The rapid advances of the last two or three decades in medical imaging technology, which have delivered high-resolution, three-dimensional anatomical and physiological images, is continuing apace, enabling ever more powerful advances in diagnosis and intervention. Improved, miniature detectors are pushing spatial resolution below 1 mm, which will require large computer memories and storage capacities and improved software capabilities to visualize the larger data sets interactively. Advances in post-processing, especially in automated registration, segmentation, classification and rendering, will be required (Van Leemput et al., 1999; Huber and Hebert, 2003; Way et al., 2006). The availability of multimodality imaging, such as combined CT/PET scanners, is increasing, along with the means to share such images around the clinical setting and remotely, fueling improvements in PACS and telemedicine systems (Section 4.3).

    The inverse problem

    A basic aspect of most imaging modalities is to reconstruct an image based on minimally invasive measurements from a number of sensors. The inverse problem determines the properties of the unknown system from the observed measurements. The goal of the reconstruction can be either structural information, such as the anatomy that comes from CT or MRI imaging, or functional information from nuclear medicine imaging or electrical impedance tomography (EIT). An important key feature of inverse problems is their ill-posedness, i.e. they do not fulfil classical requirements of existence, uniqueness and stability under data perturbations. The last aspect is especially important since in the real world measurements always contain noise; approximation methods for solving inverse problems with minimal sensitivity to noise, so-called regularization methods, are being studied.

  • 7 - Image enhancement in the frequency domain

  • from Part II - Fundamental concepts of image processing
  • Geoff Dougherty
  • Book:
    Digital Image Processing for Medical Applications
    Published online:
    12 September 2018
    Print publication:
    09 April 2009, pp 194-245

  • Summary

    Overview

    A number of mathematical transformations can be applied to images to obtain information that is not readily available in the raw image. The Fourier transform is the most popular although other transforms, such as wavelets and the Gabor transform, are being increasingly used. The Fourier transform converts the spatial domain representation of an image into an alternative representation in the Fourier domain, in terms of spatial frequencies. Convolution of the input data with the point spread function of an imaging system results in the formation of an image. The convolution operation in the spatial domain is equivalent to multiplication in the Fourier domain, which is a more efficient method of performing smoothing or sharpening of an image.

    Learning objectives

    After reading this chapter you will be able to:

  • • describe how periodic waveforms consist of a linear superposition of sinusoids;

  • • explain how the Fourier transform is derived from the Fourier series;

  • • illustrate the concept of the discrete Fourier transform in two dimensions, with its dependence on sample and hold;

  • • describe the phenomenon of aliasing and apply appropriate procedures to eliminate it;

  • • outline the properties of the Fourier transform;

  • • use cross-correlation to perform template matching;

  • • obtain the spatial resolution of an imaging system both from its point spread function (PSF) and from its modulation transfer function (MTF), and show that they are equivalent;

  • • use frequency domain filters to smooth or sharpen an image while avoiding ringing artifacts;

  • • explain the need for filters in filtered backprojection and summarize the filtered backprojection algorithm;

  • • outline the properties of the Radon transform;

  • • describe the process of direct Fourier reconstruction.

    • The Fourier domain

    Although the convolution process provides a model for the formation of an image from input signals by a (linear shift-invariant) imaging system, there exists an alternative and equivalent way of modeling the process in terms of the spatial frequency content of the image. Spatial frequency is a measure of how frequently gray values change over distance.

Page 1696 of 1902