Fuzzy nonparametric DTI segmentation for robust cingulum-tract extraction

Authors:
Suyash P. Awate;Hui Zhang;James C. Gee
Affiliations:
Department of Radiology, University of Pennsylvania, Philadelphia, PA;Department of Radiology, University of Pennsylvania, Philadelphia, PA;Department of Radiology, University of Pennsylvania, Philadelphia, PA
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Year:
2007

Citing 7
Cited 2

Flexible Independent Component Analysis

Journal of VLSI Signal Processing Systems
A Riemannian Framework for Tensor Computing

International Journal of Computer Vision
A fuzzy, nonparametric segmentation framework for DTI and MRI analysis

IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
A riemannian approach to diffusion tensor images segmentation

IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Segmentation of thalamic nuclei from DTI using spectral clustering

MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Gaussian mixture density modeling, decomposition, and applications

IEEE Transactions on Image Processing
A nonparametric statistical method for image segmentation using information theory and curve evolution

IEEE Transactions on Image Processing

A nonparametric Riemannian framework on tensor field with application to foreground segmentation

Pattern Recognition
Anisotropy Preserving DTI Processing

International Journal of Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel segmentation-based approach for fiber-tract extraction in diffusion-tensor (DT) images. Typical tractography methods, incorporating thresholds on fractional anisotropy and fiber curvature to terminate tracking, can face serious problems arising from partial voluming and noise. For these reasons, tractography often fails to extract thin tracts with sharp changes in orientation, e.g. the cingulum. Unlike tractography--which disregards the information in the tensors that were previously tracked--the proposed method extracts the cingulum by exploiting the statistical coherence of tensors in the entire structure. Moreover, the proposed segmentation-based method allows fuzzy class memberships to optimally extract information within partial-volumed voxels. Unlike typical fuzzy-segmentation schemes employing Gaussian models that are biased towards ellipsoidal clusters, the proposed method models the manifolds underlying the classes by incorporating nonparametric data-driven statistical models. Furthermore, it exploits the nonparametric model to capture the spatial continuity and structure of the fiber bundle. The results on real DT images demonstrate that the proposed method extracts the cingulum bundle significantly more accurately as compared to tractography.