Tools and Algorithms for Classification in Volume Rendering of Dual Energy Data Sets
In the last few years, technology within the medical imaging sector has made many advances, which in turn has opened many new possibilities. One such recent advance is the development of imaging with data from dual energy computed tomography, CT, scanners. However, with new possibilities come new challenges.
One challenge, that is discussed in this thesis, is rendering of images created from two volumes in an efficient way with respect to the classification of the data. Focus lies on investigating how dual energy data sets can be classified in order to fully use the potential of having volumes from two different energy levels. A critical asset in this investigation is the ability to utilize a transfer function description that extends into two dimensions. One such transfer function description is presented in detail.
With this two-dimensional description comes the need for a new way to interact with the transfer function. How the interaction between a user and the transfer function description is implemented for Siemens Corporate Research in Princeton, NJ will also be discussed in this thesis as well as the classification results of rendering dual energy data. These results show that it is possible to classify blood vessels correctly when rendering dual energy computed tomography angiography, CTA, data.
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