1 May 1994 Generalized transfer functions and their use for three-dimensional neuroimaging
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Volume rendering uses classification based on fuzzy segmentation, and the key to making meaningful images lies in a proper choice of the opacity and color transfer functions. The smooth and continuous transfer functions introduce less artifacts than any binary operations such as thresholding which disrupts the continuity of the data. Despite this advantage, density based fuzzy segmentation still has several limitations. To find suitable transfer functions for real clinical data may be a laborious task, and methods facilitating the automated generation of transfer functions are very useful. Furthermore, the standard transfer functions are based on the density of a resampled point. This results in several shortcomings. To overcome those limitations, we propose a suitable extension of the standard transfer functions called generalized transfer functions. These functions use both density based as well as non-density based information about classified structures. We show the usefulness of the generalized transfer functions in 3D neuroimaging (neuropathology) from MRI data. Three approaches are discussed: contour-enhanced volume rendering, ROIs-enhanced volume rendering, and slice density corrected transfer functions.
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Wieslaw L. Nowinski, Wieslaw L. Nowinski, Ho Wee Chong, Ho Wee Chong, } "Generalized transfer functions and their use for three-dimensional neuroimaging", Proc. SPIE 2164, Medical Imaging 1994: Image Capture, Formatting, and Display, (1 May 1994); doi: 10.1117/12.174022; https://doi.org/10.1117/12.174022

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