Robust Tract Skeleton Extraction of Cingulum Based on Active Contour Model from Diffusion Tensor MR ImagingReportar como inadecuado

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Journal Title:



Volume 8, Number 2


Public Library of Science | 2013-02-26, Pages e56113-e56113

Type of Work:

Article | Final Publisher PDF

Abstract: Cingulum is widely studied in healthy and psychiatric subjects. For cingulum analysis from diffusion tensor MR imaging, tractography and tract of interest method have been adopted for tract-based analysis. Because tractography performs fiber tracking according to local diffusion measures, they can be sensitive to noise and tracking errors can be accumulated along the fiber. For more accurate localization of cingulum, we attempt to define it by skeleton extraction using the tensors' information throughout the tract of cingulum simultaneously, which is quite different from the idea of tractography. In this study, we introduce an approach to extract the skeleton of cingulum using active contour model, which allows us to optimize the location of cingulum in a global sense based on the diffusion measurements along the entire tract and contour regularity. Validation of this method on synthetic and experimental data proved that our approach is able to reduce the influence of noise and partial volume effect, and extract the skeleton of cingulum robustly and reliably. Our proposed method provides an approach to localize cingulum robustly, which is a very important feature for tract-based analysis and can be of important practical utility.

Subjects: Engineering, Biomedical - Health Sciences, General - Research Funding: This work was kindly supported by the National Institutes of Health PO1AG026423, P50MH058922 and the National Natural Science Foundation of China 30970769, 81171314.

Keywords: Science and Technology - Multidisciplinary Sciences - Science and Technology - Other Topics - MULTIDISCIPLINARY SCIENCES - WHITE-MATTER - HUMAN BRAIN - TRACTOGRAPHY - TRACKING - SEGMENTATION - CONNECTIVITY - DEFLECTION - CORTEX -

Autor: Wu Li, Xiaoping Hu,



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