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Identifying Group-Wise Consistent White Matter Landmarks via Novel Fiber Shape Descriptor
Hanbo Chen1, Tuo Zhang1, 2, and Tianming Liu1
1Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
2School of Automation, Northwestern Polytechnical University, Xi’an, China
Abstract. Identification of common and corresponding white matter (WM) regions of interest (ROI) across human brains has attracted growing interest because it not only facilitates comparison among individuals and populations, but also enables the assessment of structural/functional connectivity in populations. However, due to the complexity and variability of the WM structure and a lack of effective white matter streamline descriptors, establishing accurate correspondences of WM ROIs across individuals and populations has been a challenging open problem. In this paper, a novel fiber shape descriptor which can facilitate quantitative measurement of fiber bundle profile including connection complexity and similarity has been proposed. A novel framework was then developed using the descriptor to identify group-wise consistent connection hubs in WM regions as landmarks. 12 group-wise consistent WM landmarks have been identified in our experiment. These WM landmarks are found highly reproducible across individuals and accurately predictable on new individual subjects by our fiber shape descriptor. Therefore, these landmarks, as well as proposed fiber shape descriptor has shown great potential to human brain mapping.
Keywords: School of Automation, Northwestern Polytechnical University, Xi’an, China
LNCS 8149, p. 66 ff.
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© Springer-Verlag Berlin Heidelberg 2013