Image Processing Reference
In-Depth Information
23.3.2 Heterogeneous Display and Computing Devices
Mobile devices, in particular the Apple products iPad and iPhone, are extremely
popular among medical doctors and indeed solve some serious problems of desktop
devices in routine clinical use. In particular, bedside use of patient data is an essential
use case for medical doctors of various disciplines.
Meanwhile, several mobile devices are equipped with powerful graphics cards
and, using the OpenGL ES (Embedded Systems) standard, they are able to provide
high-quality interactive rendering. Although the performance still trails that of mod-
ern desktop devices, slicing medical volume data and 3D rendering is feasible [
52
].
The rapid and widespread use of mobile devices also made gesture input popular.
In particular, multi-touch interaction is considered an intuitive interaction since many
potential users know a variety of gestures from their everyday activities with smart
phones. Therefore, multitouch interaction is also incorporated in large displays in
medical use, e.g. the Digital Lightbox
1
by BrainLab and the multi-touch table of
Lundström et al. [
50
].
23.3.3 Interactive Image Segmentation
Image segmentation is important in clinical practice, for example, in diagnosis and
therapy planning, and also in image-based medical research. In these applications,
segmentation is complicated by the great deal of variation in image acquisition,
pathology and anatomy. Furthermore, in matters of diagnosis or therapy planning,
the accuracy of the segmentation can be critical. It comes as no surprise that user
interaction is often required to help ensure the quality of the results, by initializing
an image processing method, checking the accuracy of the result or to correct a
segmentation [
55
].
A well-known interactive segmentation technique is the live-wire or intelligent
scissors [
51
]. These ideas were later extended and applied to medical images [
21
].
More recently, visualization has been applied to the challenge of explicitly dealing
with the uncertainty inherent in interactive 3D segmentation [
56
,
60
].
Medical visualization research often combines elements of image analysis, graph-
ics and interaction, and is thus ideally equipped to address the challenge of developing
and validating effective interactive segmentation approaches for widespread use in
medical research and practice.
23.3.4 Topological Methods
Topological data representation has played an important role inmedical visualization,
since it can allow us to segment specific features such as human organs and bones
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