@inbook {eichelbaum2010b,
	title = {Image-space Tensor Field Visualization Using a LIC-like Method},
	booktitle = {Visualization in Medicine and Life Sciences 2},
	series = {Mathematics and Visualization},
	year = {2012},
	pages = {193{\textendash}210},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	abstract = {Tensors are of great interest to many applications in engineering and in medical imaging, but a proper analysis and visualization remains challenging. Physics-based visualization of tensor fields has proven to show the main features of symmetric second-order tensor fields, while still displaying the most important information of the data, namely the main directions in medical diffusion tensor data using texture and additional attributes using color-coding, in a continuous representation. Nevertheless, its application and usability remains limited due to its computational expensive and sensitive nature. We introduce a novel approach to compute a fabric-like texture pattern from tensor fields motivated by image-space line integral convolution (LIC). Although, our approach can be applied to arbitrary, non-selfintersecting surfaces, we are focusing on special surfaces following neural fibers in the brain. We employ a multi-pass rendering approach whose main focus lies on regaining three-dimensionality of the data under user interaction as well as being able to have a seamless transition between local and global structures including a proper visualization of degenerated points.},
	author = {Eichelbaum, Sebastian and Mario Hlawitschka and Bernd Hamann and Gerik Scheuermann},
	editor = {Lars Linsen and Bernd Hamann and Hans Hagen and Hege, H.-C.}
}
