10.11.2014 Views

Master's Thesis - Studierstube Augmented Reality Project - Graz ...

Master's Thesis - Studierstube Augmented Reality Project - Graz ...

Master's Thesis - Studierstube Augmented Reality Project - Graz ...

SHOW MORE
SHOW LESS

Create successful ePaper yourself

Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.

2.1 Technical Visualization<br />

cial needs of medical image data and physiological flow data. The upcoming sections<br />

identify dedicated visualization techniques for both.<br />

2.1.1.1 Representation Techniques for Volumetric Morphological Data<br />

To be able to perform medical volumetric data visualization one first has to consider the<br />

underlying two dimensional images which build-up an image stack for further volumetric<br />

approaches together. The following considerations refer predominantly to morphological<br />

data which means images presenting an accurate representation for the shape of<br />

different tissues.<br />

Displaying even two dimensional medical image data is in general not as trivial as decoding<br />

and presenting an image in a common format. Unlike a picture taken with an ordinary<br />

picturing device providing red, green and blue components R[0...255], G[0...255],<br />

B[0...255], structural projection or scanning techniques deliver only gray values (R =<br />

G = B) but with a greater range. In a commonly known medical imaging format called<br />

Digital Imaging and Communications in Medicine (DICOM), [DICOM2007], the intensity<br />

values are encoded with 12 bits, which allows to encode more information in<br />

one pixel. The problem hereby is the limitation of all display devices to 256 values and<br />

the constraints on the human visual system. [Barlow1956; Lu1999; Barten1992] have<br />

identified these constraints by estimating the required signal stimulus energy required<br />

for an observer to maintain a perception and found that the perception is dependent<br />

on a non-linear function. Due to these limitations a windowing mechanism has been<br />

developed among others by [NEMA2003] to provide a mapping from the measured 12-<br />

bit data to a 256 gray value gradient. Such a mapping is called windowing and briefly<br />

outlined in figure 2.5.<br />

This constraint of pixel value mapping has to be kept in mind when working on<br />

medical data, especially when using them with some special volumetric rendering techniques<br />

for image stacks as they are appearing next. Later used techniques, which are<br />

described in more detail, use transfer functions which implicate the windowing in their<br />

parameters. The remaining rendering techniques are itemized in the end of this section<br />

along with the definition of volume rendering transfer functions.<br />

10

Hooray! Your file is uploaded and ready to be published.

Saved successfully!

Ooh no, something went wrong!