6
Other Signal and Image
Processing Methods
6.1 INTRODUCTION AND OVERVIEW
In this section, some other techniques used in biomedical signal and image processing are discussed. In order to avoid an excessively long chapter, the introduced
techniques are described in a brief and concise manner. The first part of this chapter
deals with the complexity measures computed mainly for one-dimensional (1-D) signals and their roles in biomedical signal processing. The second part of the chapter
focuses on an important transformation in signal and image processing called cosine
transform. In addition, a part of this chapter is dedicated to a brief review of coding
and information theory, which is heavily used in both signal and image processing.
Finally, a brief review of the methods for coregistration of images is presented.
6.2 COMPLEXITY ANALYSIS
A main characteristic of the biomedical and biological systems is their high complexity. For instance, complexity is often considered as the key feature that allows
biomedical and biological systems to adapt to the dramatic environmental changes.
In processing of a biomedical system, it is often the case that the complexity of the
signals created by the system needs to be identified and evaluated using signal processing method. Evaluation of the biomedical complexity has become an important
factor in diagnostics of the biomedical systems. A rule of thumb in biomedical sciences states that the normal and healthy biomedical systems are often very complex,
and once a disease or abnormality occurs, the complexity of the system drops. An
example of this rule is the significant decrease in all complexity measures of electroencephalogram (EEG) in diseased cases such as epilepsy (compared to the normal
EEG). The same rule is applicable in other physiological units such as cardiovascular
system, where a sharp drop in electrocardiogram (ECG) is associated with diseases
such as flutter. These observations will be further described in Part II of the book.
Knowing the importance of the complexity measures in biomedical signals and
systems, we start the description of some popular complexity measures with two
local measures of complexity: “signal complexity” and “signal mobility.”
6.2.1 SIGNAL COMPLEXITY AND SIGNAL MOBILITY
These two features can quantitatively measure the level of variations along a signal. They are often used in the analysis of biomedical signals quantifying the firstand second-order variations in signals. Signal mobility addresses the normalized
101
Other Signal and Image
Processing Methods
6.1 INTRODUCTION AND OVERVIEW
In this section, some other techniques used in biomedical signal and image processing are discussed. In order to avoid an excessively long chapter, the introduced
techniques are described in a brief and concise manner. The first part of this chapter
deals with the complexity measures computed mainly for one-dimensional (1-D) signals and their roles in biomedical signal processing. The second part of the chapter
focuses on an important transformation in signal and image processing called cosine
transform. In addition, a part of this chapter is dedicated to a brief review of coding
and information theory, which is heavily used in both signal and image processing.
Finally, a brief review of the methods for coregistration of images is presented.
6.2 COMPLEXITY ANALYSIS
A main characteristic of the biomedical and biological systems is their high complexity. For instance, complexity is often considered as the key feature that allows
biomedical and biological systems to adapt to the dramatic environmental changes.
In processing of a biomedical system, it is often the case that the complexity of the
signals created by the system needs to be identified and evaluated using signal processing method. Evaluation of the biomedical complexity has become an important
factor in diagnostics of the biomedical systems. A rule of thumb in biomedical sciences states that the normal and healthy biomedical systems are often very complex,
and once a disease or abnormality occurs, the complexity of the system drops. An
example of this rule is the significant decrease in all complexity measures of electroencephalogram (EEG) in diseased cases such as epilepsy (compared to the normal
EEG). The same rule is applicable in other physiological units such as cardiovascular
system, where a sharp drop in electrocardiogram (ECG) is associated with diseases
such as flutter. These observations will be further described in Part II of the book.
Knowing the importance of the complexity measures in biomedical signals and
systems, we start the description of some popular complexity measures with two
local measures of complexity: “signal complexity” and “signal mobility.”
6.2.1 SIGNAL COMPLEXITY AND SIGNAL MOBILITY
These two features can quantitatively measure the level of variations along a signal. They are often used in the analysis of biomedical signals quantifying the firstand second-order variations in signals. Signal mobility addresses the normalized
101