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1869 results in Biomedical engineering

Page 82 of 94

  • 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.

  • 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.

  • 3 - Medical images obtained with ionizing radiation

  • 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 47-89

  • Summary

    Overview

    The introduction of advanced imaging modalities has significantly improved the diagnostic information available to physicians. Computer technology has enabled tomographic and three-dimensional reconstruction of images, illustrating both anatomical features (using x-rays) and physiological functioning (using γ-rays emitted from ingested or injected radioactive tracers), free from overlying structures. Since both x-rays and γ-rays are forms of ionizing radiation, they must be used prudently in order to minimize damage to the body and its genetic material.

    Learning objectives

    After reading this chapter you will be able to:

  • • explain the basis of imaging using x-rays and γ-rays;

  • • outline the physical factors involved in imaging modalities using ionizing radiation;

  • • identify the factors that affect image quality in imaging systems that use ionizing radiation;

  • • explain the advantages of computed radiography over film radiography;

  • • describe the specific challenges in mammography and explain how they are addressed;

  • • describe the imaging pathway in fluoroscopy;

  • • explain the advantages and limitations of digital subtraction angiography;

  • • distinguish planar imaging from topographic imaging;

  • • reconstruct a simple x-ray tomographic image using backprojection;

  • • explain how the production of a tomographic image in single-photon emission tomography (SPECT) differs from that in x-ray computed tomography (CT);

  • • identify the organs and tissues most sensitive to damage by ionizing radiation.

    • Medical imaging modalities

    Medical imaging systems detect different physical signals arising from a patient and produce images. An imaging modality is an imaging system which uses a particular technique. Some of these modalities use ionizing radiation, radiation with sufficient energy to ionize atoms and molecules within the body, and others use non-ionizing radiation. Ionizing radiation in medical imaging comprises x-rays and γ-rays, both of which need to be used prudently to avoid causing serious damage to the body and to its genetic material. Non-ionizing radiation, on the other hand, does not have the potential to damage the body directly and the risks associated with its use are considered to be very low. Examples of such radiation are ultrasound, i.e. high-frequency sound, and radio frequency waves.

  • 13 - Medical applications of imaging

  • 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 381-394

  • Summary

    Overview

    Imaging science visualizes an object and quantitatively characterizes its structure and/or function. Biomedical imaging applies imaging science to the presentation of and interaction with multi-modality biomedical images with a view to using them productively to examine and diagnose disease in human patients. This chapter discusses a number of specific applications in medicine that illustrate many of the concepts introduced in this book. The examples have been chosen to demonstrate a wide range of algorithms and approaches; none represent complete solutions, but are rather examples of continuing research.

    Learning objectives

    After reading this chapter you will be able to:

  • • appreciate the complexity and problems associated with imaging tasks;

  • • recognize broad schemes for approaching image analysis;

  • • analyze the component parts in an imaging problem;

  • • select potential strategies for analyzing images from a variety of applications.

    • Computer-aided diagnosis in mammography

    Mammography (Section 3.2.3) is the single most important technique in the investigation of breast cancer, the most common malignancy in women. It can detect disease at an early stage when therapy or surgery is most effective. However the interpretation of screening mammograms is a repetitive task involving subtle signs, and suffers from a high rate of false negatives (10–30% of women with breast cancer are falsely told that they are free of the disease on the basis of their mammograms (Martin, Moskowitz and Milbrath, 1979)), and false positives (only 10–20% of masses referred for surgical biopsy are actually malignant (Kopans, 1992)). Computer-aided diagnosis (CAD) aims to increase the predictive value of the technique by pre-reading mammograms to indicate the locations of suspicious abnormalities, and analyze their characteristics, as an aid to the radiologist.

  • 4 - Medical images obtained with non-ionizing radiation

  • 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 90-120

  • Summary

    Overview

    Diagnostic medical ultrasound uses high-frequency sound and a simple pulse–echo technique. When an ultrasound beam is swept across a volume of interest, a crosssectional image can be formed from a mapping of echo intensities. Current medical ultrasound imaging systems are based on envelope detection, and therefore only display intensity information. Despite this shortcoming, ultrasound imaging has become an important and widely accepted modality for non-invasive imaging of the human body because of its ability to produce real-time images, its low cost and its low risk to the patient. Magnetic resonance imaging (MRI) uses the phenomenon of nuclear magnetic resonance (NMR): unpaired nucleons, such as protons, orientate themselves in a magnetic field, and radiofrequency pulses can be used to change the balance of the orientations. When the system returns to equilibrium it produces signals that can be used to produce an image, which is characterized by its high contrast for soft tissues. MRI images map function, as well as structure. Digital images from any imaging modality can be compared or combined, after image registration, using a networking system.

    Learning objectives

    After reading this chapter you will be able to:

  • • explain the basis of imaging using non-ionizing radiation, specifically ultrasound and radiofrequency (RF) radiation with a strong magnetic field;

  • • outline the physical factors involved in these imaging modalities;

  • • describe the factors which determine the speed of ultrasound waves in a material;

  • • explain the purpose of time gain compensation and describe how it is implemented;

  • • summarize the steps involved in the reconstruction of B-mode ultrasound images;

  • • identify the factors that affect image quality and artifacts in ultrasound imaging;

  • • describe the phenomenon of nuclear magnetic resonance (NMR);

  • • explain how MRI images can be constructed from NMR spectra;

  • • describe the use of magnetic field gradients to add spatial information to MRI images;

  • • summarize the changes that occur to the spins using the spin echo pulse sequence;

  • • identify the factors that affect image quality and artifacts in MRI imaging;

  • • describe how functional information can be obtained from MRI imaging;

  • • summarize the advantages of a picture, archiving and communications system (PACS);

  • • outline the factors involved in the co-registration of images from different modalities.

  • 8 - Image restoration

  • 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 246-270

  • Summary

    Overview

    An image is never an exact representation of the object under observation; it is always corrupted by degradations during acquisition and within the imaging system itself. These include noise, blurring and distortion. Image restoration removes or reduces these degradations. The point spread function (PSF) or the modulation transfer function (MTF) provides a complete, quantitative description of an imaging system and directly characterizes the image degradation within the system and can be used to restore the fine detail in images. The problem is more complicated if the image is also degraded by significant amounts of noise. Restoration techniques attempt to model the degradation and apply the inverse process to recover the original image. They are most effective when the point spread function or modulation transfer function is known and the nature of the blurring and noise are well understood. Geometric distortions can be reversed using inverse bilinear equations and gray-level interpolation.

    Learning objectives

    After reading this chapter you will be able to:

  • • identify the main sources of image noise and discuss their characteristics;

  • • choose appropriate general strategies for minimizing the effects of noise;

  • • discuss the advantages of adaptive filtering;

  • • model image degradation comprising blur and additive noise;

  • • employ suitable values to Wiener filter a noisy, blurred image;

  • • compare the performance of inverse filtering with Wiener filtering;

  • • explain how distortion can be removed from images.

    • Image degradation

    Images can be degraded by a number of different mechanisms, including noise, blurring and distortion. Noise is present because any imaging device must use a finite exposure (or integration) time, which introduces stochastic noise from the random arrival of photons. Optical imperfections and instrumentation noise (for example, thermal noise in CCD devices) result in more noise. Sampling causes noise due to aliasing of high-frequency signal components, and digitization produces quantization errors. Further noise can be introduced by communication errors and compression. Blurring is present in any imaging system which uses electromagnetic radiation (for example, visible light and x-rays).

Page 82 of 94