Biomedical Engineering Reference
In-Depth Information
and quality of visualization are critical here, and so is the
paradigm of interaction. Some of the technology needed
for such visualization applications may not be available
today, but there is rapid progress in this area.
6.6.2.1 First generation systems
Real-time patient monitoring
Prior to the arrival of image-based information in radi-
ology, the most basic forms of visualization in bio-
medicine were the ID waveforms that one would see on
such devices as ECG monitors. Although elementary,
this form of visualization is a very powerful tool for
conveying the physiological state of the subject during
a clinical intervention. Depending on the time scale of
these waveforms, one would be able to understand both
the present state of the subject and the trend condition.
This information may be difficult to represent in any
other form other than simple visual aid.
Subsequently, more advanced forms of these ID dis-
plays were generated to convey the combined physio-
logical state using different signals such as ECG, blood
pressure, and respiratory waveforms. These various sig-
nals were appropriately combined and presented in
a form that would help clinicians rapidly observe po-
tential problems during a clinical intervention. Thus, the
''multimodality'' of information helped clinicians un-
derstand the ''functional'' state of the physiology of
the subject. These early developments already indicated
the potential benefit of visualization in patient care.
Anesthesiologists who operate various instruments for
delivering and maintaining proper respiratory and he-
modynamic state during an intraoperative procedure may
need to read various displays to be careful to avoid
human errors. Their task is often described to be analo-
gous to that of a pilot inside a cockpit. As heads-up
display visualization techniques helped revolutionize the
organization of the cockpit, the use of high-end visuali-
zation became common for even the simplest forms of
biomedical signals. Thus, the term cockpit visualization
technology became popular for describing the impact of
visualization in medicine.
6.6.1.2 Visualization genotypes
In medicine, because of the inherent complexity in vi-
sualizing the information in the data, different concepts
of visualization evolved as the technologies that could
enable them became available. For the purpose of this
discussion, the evolution of different concepts of visu-
alization in medicine can be grouped into several
generations.
First generation systems are essentially one-
dimensional (ID) waveform displays such as those
that appear in patient monitoring systems.
Second generation systems perform two-dimensional
(2D) image processing and display. Contours and
stack of contour lines that can represent the three-
dimensional form of the data also were developed
during this period.
Third generation systems generally involve 3D image
processing and visualization. Isosurfaces, contour
surfaces, shell-rendering and volume-rendering
techniques were developed in this generation.
Fourth generation systems process multidimensional
data such as dynamic volume data sets, sometimes
called 4D data. In general the fourth dimension can
be any other dimension associated with volume data.
Fifth generation systems are virtual reality type
visualization systems, which combine
multidimensional data with 3D (i.e., six degree of
freedom) interaction.
Next generation systems represent concepts under
development, such as sensory feedback techniques
where the user interacting with the structures could
feel the physical properties of the material and
obtain valuable ''visual-sensory'' information in
simulation type visualization systems.
The main focus of this chapter is on investigative and
imitative visualization. Clinical research examples from
medicine and biology are provided to illustrate the
concepts of visualization and their significance.
6.6.2.2 Second generation systems
2D image processing techniques and displays formed the
second generation systems. Some of the earliest 2D vi-
sualization tools were image processing techniques for
enhancing image features that otherwise may have been
ignored. Feature extraction techniques, expert systems,
and neural networks applications were developed along
with second generation visualization systems.
6.6.2 Illustrative visualization
Interpolation
In medical images, the number of pixels might vary
depending on the modality of imaging, and it is usually in
the range of 128
128 to 512
512. The resolution of
graphic displays is usually high, above 72 pixels/inch,
making these images appear relatively small. Suitable
This class of visualization is the earliest one to develop
and includes the first three generations, with their ID,
2D and 3D visualization displays. The concepts de-
veloped here are also applied in the other two pheno-
types of visualization.
