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Proceedings Paper

Rule-based fuzzy vector median filters for 3D phase contrast MRI segmentation
Author(s): Kartik S. Sundareswaran; David H. Frakes; Ajit P. Yoganathan
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Paper Abstract

Recent technological advances have contributed to the advent of phase contrast magnetic resonance imaging (PCMRI) as standard practice in clinical environments. In particular, decreased scan times have made using the modality more feasible. PCMRI is now a common tool for flow quantification, and for more complex vector field analyses that target the early detection of problematic flow conditions. Segmentation is one component of this type of application that can impact the accuracy of the final product dramatically. Vascular segmentation, in general, is a long-standing problem that has received significant attention. Segmentation in the context of PCMRI data, however, has been explored less and can benefit from object-based image processing techniques that incorporate fluids specific information. Here we present a fuzzy rule-based adaptive vector median filtering (FAVMF) algorithm that in combination with active contour modeling facilitates high-quality PCMRI segmentation while mitigating the effects of noise. The FAVMF technique was tested on 111 synthetically generated PC MRI slices and on 15 patients with congenital heart disease. The results were compared to other multi-dimensional filters namely the adaptive vector median filter, the adaptive vector directional filter, and the scalar low pass filter commonly used in PC MRI applications. FAVMF significantly outperformed the standard filtering methods (p < 0.0001). Two conclusions can be drawn from these results: a) Filtering should be performed after vessel segmentation of PC MRI; b) Vector based filtering methods should be used instead of scalar techniques.

Paper Details

Date Published: 26 February 2008
PDF: 14 pages
Proc. SPIE 6814, Computational Imaging VI, 68140F (26 February 2008); doi: 10.1117/12.776498
Show Author Affiliations
Kartik S. Sundareswaran, Georgia Institute of Technology (United States)
David H. Frakes, 4D Imaging Inc. (United States)
Ajit P. Yoganathan, Georgia Institute of Technology (United States)

Published in SPIE Proceedings Vol. 6814:
Computational Imaging VI
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)

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