Visualization of Fibrous and Thread-like Data

Zeki Melek, David Mayerich, Cem Yuksel, John Keyser
Department of Computer Science, Texas A&M University, College Station, TX
IEEE Transactions on Visualization and Computer Graphics, Vol. 12, No. 5, 2006


   title={Visualization of fibrous and thread-like data},

   author={Melek, Z. and Mayerich, D. and Yuksel, C. and Keyser, J.},

   journal={IEEE Transactions on Visualization and Computer Graphics},



   publisher={Published by the IEEE Computer Society}


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Thread-like structures are becoming more common in modern volumetric data sets as our ability to image vascular and neural tissue at higher resolutions improves. The thread-like structures of neurons and micro-vessels pose a unique problem in visualization since they tend to be densely packed in small volumes of tissue. This makes it difficult for an observer to interpret useful patterns from the data or trace individual fibers. In this paper we describe several methods for dealing with large amounts of thread-like data, such as data sets collected using knife-edge scanning microscopy (KESM) and serial block-face scanning electron microscopy (SBF-SEM). These methods allow us to collect volumetric data from embedded samples of whole-brain tissue. The neuronal and microvascular data that we acquire consists of thin, branching structures extending over very large regions. Traditional visualization schemes are not sufficient to make sense of the large, dense, complex structures encountered. In this paper, we address three methods to allow a user to explore a fiber network effectively. We describe interactive techniques for rendering large sets of neurons using self-orienting surfaces implemented on the GPU. We also present techniques for rendering fiber networks in a way that provides useful information about flow and orientation. Third, a global illumination framework is used to create high-quality visualizations that emphasize the underlying fiber structure. Implementation details, performance, and advantages and disadvantages of each approach are discussed
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