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Medical imaging technologies enable views of the internal structure and function of the human body. Information obtained from the various modalities can be used to diagnose abnormalities, guide therapeutic procedures, and monitor disease treatment. In this chapter, a light introduction about various medical imaging modalities with four kinds of classification approaches is given.

1.1 Classifications of Electromagnetic Radiation Spectrum

Electromagnetic radiation consists of alternating electric and magnetic fields. In an electromagnetic wave, these fields are directed perpendicular to each other and to the direction of propagation. They are classified by the frequency ν and wavelength λ. In free space, all electromagnetic waves travel with the speed of light, c ≈ 3×108 ms-1. The propagation speed establishes the relation between wavelength λ and frequency ν of an electromagnetic wave as

λν = c.

The frequency is measured in cycles per second (Hz or s-1) and the wavelength in meters (m).

Figure 1-1 illustrates that electromagnetic waves span an enormous frequency and wavelength range of 24 decades, and only a tiny fraction from about 400-700 nm, about one octave, falls in the visible region to the human eye. In matter, the electric and magnetic fields of the electromagnetic wave interact with the electric charges, electric currents, electric fields, and magnetic fields in the medium. Nonetheless, the basic nature of electromagnetic waves remains the same, only the propagation of the wave is slowed down and the wave is attenuated. Electromagnetic waves are generally a linear phenomenon. This means that we can decompose any complex wave pattern into basic ones such as plane harmonic waves. Or, conversely, we can superimpose any two or more electro- magnetic waves and be sure that they are still electromagnetic waves.

Most images in clinical medicine are generated by recording the physical properties of tissue when being exposed to a certain type of electromagnetic radiation (ER); Or in the case of ultrasound - mechanical excitation. An illustration is given in Figure 1-1. Electromagnetic energy is quantized in that for a given frequency its energy can only occur in multiples of the quantity hν in which h is Planck's constant, the action quantum. ER consists of quantum objects, so called photons, and the energy of a photon can be formulated as follows:

E = hv[J]

where h is Planck's constant, set as 6.626 *10-34 Js (the unit Js is the physical quantity of Action), and v is the frequency of photon (the number of oscillations of a wave per second.); its unit is Hertz and equivalent to the reciprocal value of time: 1Hz = 1/s. The product of action and frequency is an energy: Js/s = J.

The energy of the photon is often given in the energy unit electron volts (eV). This is the kinetic energy an electron would acquire in being accelerated through a potential difference of one volt. Figure 1-1 includes a photon energy scale in eV. The higher the frequency of electromagnetic radiation, the more its particulate nature becomes apparent, because its energy quanta get larger. The energy of a photon can be larger than the energy associated with the rest mass of an elementary particle.

The quantization of the energy of electromagnetic waves is important for imaging since sensitive radiation detectors can measure the absorption of a single photon. Such detectors are called photon counters. Thus, the lowest energy amount that can be detected is hν. The random nature of arrival of photons at the detector gives rise to an uncertainty ("noise") in the measurement of radiation energy. The number of photons counted per unit time is a random variable with a Poisson distribution. The random process of light emission can generally be modeled using a Poisson distribution, the properties of which are very well known. If we note p(n) the probability that n photons arrive on the detector.

where σn is the standard deviation. What this means is that for 100 photons arriving on the detector, the uncertainty about the number of photon is of ±10 (±10%). If the number of photon is somewhat closer to common levels, e.g. 1010 , the uncertainty becomes ±105, which is ±0.000,01%. It then becomes obvious that the shot noise is an issue only at low light level.

In Electrical impedance tomography (EIT), an image of the conductivity or permittivity of part of the body is inferred from surface electrical measurements. A typical electrical imaging system uses a system of conducting electrodes attached to the surface of the body under investigation. One can apply current or voltage to these electrodes and measure voltage or current respectively. EIT applications include monitoring of lung function [7], detection of cancer in the skin and breast [10], location of epileptic foci [8] and imaging of brain activity [9]. In biological tissue the electrical conductivity and permittivity varies between tissue types likewise depending on temperature and physiological factors. The frequency of the alternating current is sufficiently high not to give rise to electrolytic effects in the body and the Ohmic power dissipated is sufficiently small and diffused over the body to be easily handled by the body's thermoregulatory system.

The measurements may be taken either by a single voltage measurement circuit multiplexed over the electrodes or a separate circuit for each electrode. Many recent systems convert the alternating signal directly, the demodulation then being performed digitally. Many EIT systems are capable of working at several frequencies and can measure both the magnitude and phase of the voltage, which are passed to a computer to perform the reconstruction and display of the image.

Emission of electromagnetic radiation occurs at any temperature and is thus a ubiquitous form of interaction between matter and electromagnetic radiation. The cause for the spontaneous emission of electromagnetic radiation is thermal molecular motion, which increases with temperature. During emission of radiation, thermal energy is converted to electromagnetic radiation and the matter is cooling down according to the universal law of energy conservation. According to the laws of thermodynamics, the fraction of radiation at a certain wavelength that is absorbed must also be re-emitted. A perfect absorber, which is a maximal emitter, is called a blackbody, of which the emitted radiation does not depend on the viewing angle. In real life, objects emit less radiation than a blackbody, and the ratio of the emission of a real body to the emission of the blackbody is called emissivity θ and depends on the wavelength.

Radiation in the infrared and microwave range can be used to image the temperature distribution of objects. This application of imaging is known as thermography. Thermal imaging is complicated by the fact that real objects are not perfect black bodies. Thus they partly reflect radiation from the surrounding. If an object has emissivity θ, a fraction 1 - θ of the received radiation originates from the environment, biasing the temperature measurement. Thermal imaging medical applications include noncontact dry eye detection [5] and detection of abnormal breast patterns [6].

Medical imaging technologies enable views of the internal structure and function of the human body. Information obtained from the various modalities can be used to diagnose abnormalities, guide therapeutic procedures, and monitor disease treatment. In this chapter, a light introduction about various medical imaging modalities with four kinds of classification approaches is given.

1.1 Classifications of Electromagnetic Radiation Spectrum

Electromagnetic radiation consists of alternating electr...

Contents
Preface
Chapter 1 Introduction to Medical Imaging
1.1 Classifications of Electromagnetic Radiation Spectrum
1.2 Classification of Diagnostic Medical Imaging by the Tissue Property Measured (anatomic structure or molecular function)
1.3 Classification of Diagnostic Medical Imaging by the Sources (Ionizing or non-ionizing radiation)
1.4 Classification of Diagnostic Medical Imaging by the Acquisition Mode (planar imaging or cross-sectional imaging)
Chapter 2 Digital Image
2.1 Digital Image Formation
2.2 Image Coordinate System
2.3 Color Image Format
2.4 Color Space and Color Conversion
2.5 Neighborhood Relations
2.6 Image Resolution
Chapter 3 DICOM and PACS
3.1 Introduction
3.2 Hexadecimal (binary) Format (16-bit)
3.3 Little Endian (default byte-ordering type)
3.4 DICOM Data Dictionary
3.5 Extract Information Using Matlab
3.6 Storing Image Data
3.7 DICOM Information Hierarchy
3.8 Medical Images in DICOM
3.9 Image Compression
3.10 Access DICOM Image using Matlab
Chapter 4 Magnetic Resonance Imaging
4.1 Fundamental Concepts
4.2 Fourier Transformation for MRI Reconstruction
4.3 MR Parameters
4.4 4D Cardiac MR Image Analysis
Chapter 5 Biological Microscopic Image
5.1 Introduction
5.2 High-Throughput and High-Content Analysis
5.3 Reconstruction of Neuronal Structures
5.4 Multi-Dimensional Imaging of Dynamic Processes
5.5 Computer-Aided Microscopy Image Analysis
Chapter 6 X-ray and Computed Tomography (CT)
6.1 Introduction
6.2 Acquisition Devices in Diagnostic
6.3 How CT Scans are Performed in Clinical Practice
6.4 Principles of X-ray Computed Tomography (CT)
6.5 Projection Geometries in Computed Tomography (CT)
6.6 Public Computed Tomography Database
6.7 Computer Aided Diagnosis Applications
Chapter 7 Ultrasound Image Analysis
7.1 Introduction
7.2 Resolution
7.3 Machine Controls to Optimize Ultrasound Image
7.4 Modes of Echo Display
7.5 Interaction with Tissues and Artifacts
7.6 Advantages and Disadvantages of using Ultrasound in Medicine
7.7 Diagnostic Examination and Therapy
7.8 Automatic IVUS Image Segmentation
Chapter 8 An Open Source Image Processing Software (ImageJ / Fiji)
8.1 Introduction
8.2 ImageJ Distributions
8.3 Software Packages Built on Top of ImageJ
8.4 ImageJ Interoperability
8.5 Installing and Maintaining
8.6 Basic Image Processing
8.7 Macros, Plugins and Scripts
8.8 ImageJ Plugins for Users
8.9 ImageJ Plugins for Developers
8.10 Build ImageJ Distributions

Contents
Preface
Chapter 1 Introduction to Medical Imaging
1.1 Classifications of Electromagnetic Radiation Spectrum
1.2 Classification of Diagnostic Medical Imaging by the Tissue Property Measured (anatomic structure or molecular function)
1.3 Classification of Diagnostic Medical Imaging by the Sources (Ionizing or non-ionizing radiation)
1.4 Classification of Diagnostic Medical Imaging by the Acquisition Mode (planar imaging or cross-sectional imaging)
Chapter 2 Digital Image ...