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4473 results in Medical Imaging

Page 161 of 224

  • 4 - Data acquisition and pre-processing standards for electrical neuroimaging

  • Edited by Christoph M. Michel, Université de Genève, Thomas Koenig, Daniel Brandeis, Lorena R. R. Gianotti, Universität Zürich, Jiří Wackermann
  • Book:
    Electrical Neuroimaging
    Published online:
    15 December 2009
    Print publication:
    23 July 2009, pp 79-92

  • Summary

    Introduction

    The raw data for electrical neuroimaging is the potential field recorded on the scalp using multichannel EEG systems. Unlike waveform analysis of EEG or evoked potential (EP), electrical neuroimaging is based on the spatial analysis of these potential maps. The quality of these maps determines the goodness of the subsequent analysis steps. It is therefore of crucial importance that these scalp potential fields are recorded and pre-processed in a correct manner. An important issue concerns the number and the distribution of the electrodes on the scalp to provide an adequate spatial sampling of the potential field. Another issue is the measurement of the exact position of each electrode and the spatial normalization of the potential fields when averaging over subjects. Artifact detection and elimination is also an important point, since noise crucially influences source analysis. On the other hand, some factors that pose important problems for the traditional waveform analysis are irrelevant for electric neuroimaging. Obviously the selection of the “correct montage” for EEG analysis, or the “correct electrode” for evoked potential analysis is not relevant when analyzing the potential field. However, most important is that the question of the correct reference is completely obsolete for electrical neuroimaging. This fact was unfortunately often ignored and the “reference-problem” has been considered as a major disadvantage of the EEG compared with the MEG.

  • Preface

  • Edited by Christoph M. Michel, Université de Genève, Thomas Koenig, Daniel Brandeis, Lorena R. R. Gianotti, Universität Zürich, Jiří Wackermann
  • Book:
    Electrical Neuroimaging
    Published online:
    15 December 2009
    Print publication:
    23 July 2009, pp ix-x

  • Summary

    In 1929, Hans Berger, the founding father of electroencephalography (EEG), described EEG as a “window into the brain,” because EEG appeared to be a sensitive indicator of mental states. Eighty years later, recording and analysis methods exist that have made EEG a widespread and validated tool to observe the spatial and temporal dynamics of brain network activity during a large variety of mental states and processes in a completely noninvasive fashion. This has been made possible by significant technological advances that now allow the simultaneous recording of an EEG from a large number of electrodes at a high sampling rate, and the application of space-domain oriented approaches to the analysis of these recordings. This book gives an overview of these methods. Illustrated by various examples from experimental and clinical studies, the book is a tutorial on how to use EEG as a modern functional imaging method with the advantage of directly recording neuronal activity with millisecond temporal resolution, an approach called electrical neuroimaging.

    Electrical neuroimaging has enormous potential if properly applied, but it can also easily lead to erroneous conclusions if its basic principles are not properly understood. This book intends to give a comprehensive introduction to the basics of multichannel recording of EEG and event-related potential (ERP) data, and to spatio-temporal analysis of the potential fields. All chapters include practical examples from clinical and experimental research.

  • 3 - Imaging the electric neuronal generators of EEG/MEG

  • Edited by Christoph M. Michel, Université de Genève, Thomas Koenig, Daniel Brandeis, Lorena R. R. Gianotti, Universität Zürich, Jiří Wackermann
  • Book:
    Electrical Neuroimaging
    Published online:
    15 December 2009
    Print publication:
    23 July 2009, pp 49-78

  • Summary

    Introduction

    In order to try to understand “how the brain works,” one must make measurements of brain function. And ideally, the measurements should be as noninvasive as possible, i.e. the brain should be disturbed as little as possible during the measurement of its functions. One of the first types of noninvasive measurements reported in the literature, by Hans Berger, that directly tapped brain function was the human electroencephalogram (EEG), consisting of scalp electric potential differences as a function of time. In fact, Berger saw the EEG as a “window into the brain.” One of Berger's first observations that showed compelling evidence of having tapped brain function was the alpha rhythm. This oscillatory activity, at around 10–12 Hz, is optimally recorded from a posterior electrode with an anterior reference. The activity is very pronounced when the human subject is with eyes closed, awake, alert, resting. By simply being instructed to perform a mental task such as overtly subtracting the number seven serially, starting at 500, the alpha activity disorganizes and almost disappears.

    The main subject matter addressed in this chapter is the use of noninvasive extracranial measurements, i.e. the EEG and the magnetoencephalogram (MEG), for the estimation of the distribution in the brain of their electric neuronal generators. This can be seen as an extension of Berger's initial efforts towards developing a window into the brain.

  • 5 - Overview of analytical approaches

  • Edited by Christoph M. Michel, Université de Genève, Thomas Koenig, Daniel Brandeis, Lorena R. R. Gianotti, Universität Zürich, Jiří Wackermann
  • Book:
    Electrical Neuroimaging
    Published online:
    15 December 2009
    Print publication:
    23 July 2009, pp 93-110

  • Summary

    The general model

    The aim of this chapter is to introduce a structured overview of the different possibilities available to display and analyze brain electric scalp potentials. First, a general formal model of time-varying distributed EEG potentials is introduced. Based on this model, the most common analysis strategies used in EEG research are introduced and discussed as specific cases of this general model. Both the general model and particular methods are also expressed in mathematical terms. It is however not necessary to understand these terms to understand the chapter.

    The general model that we propose here is based on the statement made in Chapter 3, stating that the electric field produced by active neurons in the brain propagates in brain tissue without delay in time. Contrary to other imaging methods that are based on hemodynamic or metabolic processes, the EEG scalp potentials are thus “real-time,” not delayed and not a-priori frequency-filtered measurements. If only a single dipolar source in the brain were active, the temporal dynamics of the activity of that source would be exactly reproduced by the temporal dynamics observed in the scalp potentials produced by that source. This is illustrated in Figure 5.1, where the expected EEG signal of a single source with spindle-like dynamics in time has been computed. The dynamics of the scalp potentials exactly reproduce the dynamics of the source. The amplitude of the measured potentials depends on the relation between the location and orientation of the active source, its strength and the electrode position.

  • 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.

  • 1 - Introduction

  • from Part I - Introduction to image processing
  • Geoff Dougherty
  • Book:
    Digital Image Processing for Medical Applications
    Published online:
    12 September 2018
    Print publication:
    09 April 2009, pp 3-15

  • Summary

    Overview

    Imaging systems construct an (output) image in response to (input) signals from diverse types of objects. They can be classified in a number of ways, e.g. according to the radiation or field used, the property being investigated, or whether the images are formed directly or indirectly. Medical imaging systems, for example, take input signals which arise from various properties of the body of a patient, such as its attenuation of x-rays or reflection of ultrasound. The resulting images can be continuous, i.e. analog, or discrete, i.e. digital; the former can be converted into the latter by digitization. The challenge is to obtain an output image that is an accurate representation of the input signal, and then to analyze it and extract as much diagnostic information from the image as possible.

    Learning objectives

    After reading this chapter you will be able to:

  • • appreciate the breadth and scope of digital image processing;

  • • classify imaging systems according to different criteria;

  • • distinguish between analog, sampled and digital images;

  • • identify the advantages of digital imaging;

  • • describe the components of a generic digital image processing system;

  • • outline the operations involved in the various fundamental classes of image processing;

  • • list examples of digital image processing applications within a variety of fields.

    • Imaging systems

    Of the five senses – sight, hearing, touch, smell and taste – which humans use to perceive their environment, sight is the most powerful. Receiving and analyzing images forms a large part of the routine cerebral activity of human beings throughout their waking lives. In fact, more than 99% of the activity of the human brain is involved in processing images from the visual cortex. Avisual image is rich in information. Confucius said, “A picture is worth a thousand words,” and we shall see that that is an underestimate.

Digital Image Processing for Medical Applications
  • Geoff Dougherty
  • Published online:
    12 September 2018
    Print publication:
    09 April 2009
  • Image processing is a hands-on discipline, and the best way to learn is by doing. This text takes its motivation from medical applications and uses real medical images and situations to illustrate and clarify concepts and to build intuition, insight and understanding. Designed for advanced undergraduates and graduate students who will become end-users of digital image processing, it covers the basics of the major clinical imaging modalities, explaining how the images are produced and acquired. It then presents the standard image processing operations, focusing on practical issues and problem solving. Crucially, the book explains when and why particular operations are done, and practical computer-based activities show how these operations affect real images. All images, links to the public-domain software ImageJ and custom plug-ins, and selected solutions are available from www.cambridge.org/books/dougherty.

  • Preface

  • Geoff Dougherty
  • Book:
    Digital Image Processing for Medical Applications
    Published online:
    12 September 2018
    Print publication:
    09 April 2009, pp ix-xii

  • Summary

    The influence and impact of digital images on modern society, science, technology and art are tremendous. Image processing has become such a critical component in contemporary science and technology that many tasks would not be attempted without it. It is a truly interdisciplinary subject that draws from synergistic developments involving many disciplines and is used in medical imaging, microscopy, astronomy, computer vision, geology and many other fields.

    The rapid and continuing progress in computerized medical image reconstruction, and the associated developments in analysis methods and computer-aided diagnosis, have propelled medical imaging into one of the most important sub-fields in scientific imaging. This book takes its motivation from medical applications and uses real medical images and situations to clarify and consolidate concepts and to build intuition, insight and understanding. An overview of the fundamentals of the most important clinical imaging modalities in use is included to provide a context, and to illustrate how the images are produced and acquired.

    This is a text for use in a first practical course in image processing and analysis, for final-year undergraduate or first-year graduate students with a background in biomedical engineering, computer science, radiologic sciences or physics. Designed for readers who will become “end users” of digital image processing in the biomedical sciences, it emphasizes the conceptual framework and the effective use of image processing tools and uses mathematics as a tool, minimizing the advanced mathematical development of other textbooks.

    Discussions of the major medical imaging modalities enable students to understand the diagnostic tasks for which images are needed and the typical distortions and artifacts associated with each modality. This knowledge then motivates the presentation of the techniques needed to reverse distortions, minimize artifacts and enhance important features. Students understand why they are undertaking particular operations, and the practical activities enable them to see in real time how operations affect real images. Image processing is a hands-on discipline, and the best way to learn is by doing. Theory and practice are linked, each reinforcing the other.

Page 161 of 224