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Research Papers

Multi-Rigid Image Segmentation and Registration for the Analysis of Joint Motion From Three-Dimensional Magnetic Resonance Imaging

[+] Author and Article Information
Yangqiu Hu1

Department of Bioengineering,  University of Washington, Seattle, WA 98195; Department of Radiology,  University of Washington, Seattle, WA 98195

William R. Ledoux2

VA Center of Excellence for Limb Loss Prevention and Prosthetic Engineering, Seattle, WA 98108; Department of Orthopaedics & Sports Medicine,  University of Washington, Seattle, WA 98195; Department of Mechanical Engineering,  University of Washington, Seattle, WA 98195 e-mail: wrledoux@u.washington.edu VA Center of Excellence for Limb Loss Prevention and Prosthetic Engineering, Seattle, WA 98108 VA Center of Excellence for Limb Loss Prevention and Prosthetic Engineering, Seattle, WA 98108; Department of Orthopaedics & Sports Medicine,  University of Washington, Seattle, WA 98195Department of Bioengineering,  University of Washington, Seattle, WA 98195; Department of Radiology,  University of Washington, Seattle, WA 98195

Michael Fassbind, Eric S. Rohr, Bruce J. Sangeorzan, David Haynor

VA Center of Excellence for Limb Loss Prevention and Prosthetic Engineering, Seattle, WA 98108; Department of Orthopaedics & Sports Medicine,  University of Washington, Seattle, WA 98195; Department of Mechanical Engineering,  University of Washington, Seattle, WA 98195 e-mail: wrledoux@u.washington.edu VA Center of Excellence for Limb Loss Prevention and Prosthetic Engineering, Seattle, WA 98108 VA Center of Excellence for Limb Loss Prevention and Prosthetic Engineering, Seattle, WA 98108; Department of Orthopaedics & Sports Medicine,  University of Washington, Seattle, WA 98195Department of Bioengineering,  University of Washington, Seattle, WA 98195; Department of Radiology,  University of Washington, Seattle, WA 98195

1

Present address: Biomedical Genomics Core, the Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205.

2

Corresponding author. VA Puget Sound, MS 151, 1660 S. Columbian Way, Seattle WA 98108.

J Biomech Eng 133(10), 101005 (Oct 31, 2011) (8 pages) doi:10.1115/1.4005175 History: Received June 20, 2011; Revised August 29, 2011; Published October 31, 2011; Online October 31, 2011

We report an image segmentation and registration method for studying joint morphology and kinematics from in vivo magnetic resonance imaging (MRI) scans and its application to the analysis of foot and ankle joint motion. Using an MRI-compatible positioning device, a foot was scanned in a single neutral and seven other positions ranging from maximum plantar flexion, inversion, and internal rotation to maximum dorsiflexion, eversion, and external rotation. A segmentation method combining graph cuts and level set was developed. In the subsequent registration step, a separate rigid body transformation for each bone was obtained by registering the neutral position dataset to each of the other ones, which produced an accurate description of the motion between them. The segmentation algorithm allowed a user to interactively delineate 14 foot bones in the neutral position volume in less than 30 min total (user and computer processing unit [CPU]) time. Registration to the seven other positions took approximately 10 additional minutes of user time and 5.25 h of CPU time. For validation, our results were compared with those obtained from 3DViewnix, a semiautomatic segmentation program. We achieved excellent agreement, with volume overlap ratios greater than 88% for all bones excluding the intermediate cuneiform and the lesser metatarsals. For the registration of the neutral scan to the seven other positions, the average overlap ratio is 94.25%, while the minimum overlap ratio is 89.49% for the tibia between the neutral position and position 1, which might be due to different fields of view (FOV). To process a single foot in eight positions, our tool requires only minimal user interaction time (less than 30 min total), a level of improvement that has the potential to make joint motion analysis from MRI practical in research and clinical applications.

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Copyright © 2011 by American Society of Mechanical Engineers
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Figures

Grahic Jump Location
Figure 1

Segmentation graphic user interface (GUI). Three orthogonal slices through the volume are displayed, on each of which the user can draw strokes freely to mark and segment different bones (shown in different colors). The segmentation results are also represented as color overlays on the images.

Grahic Jump Location
Figure 3

Foot motion composite from eight MRI scans. (a) Top panel: anterior view, frontal plane; (b) bottom panel: lateral view, sagittal plane. From top left to bottom right: from plantar flexion, inversion, and internal rotation to dorsiflexion, eversion, and external rotation. In both panels, the upper right is the neutrally aligned scan. The tibia is fixed so all motions are relative to it.

Grahic Jump Location
Figure 2

Initial and final segmentation. (a): 2D results from graph cuts. (b): 2D results after level set refinement. (c): 3D results from graph cuts. (d): 3D results after level set refinement.

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