Vertebral shape: automatic measurement with dynamically sequenced active appearance models

Authors:
M. G. Roberts;T. F. Cootes;J. E. Adams
Affiliations:
Department of Imaging Science and Biomedical Engineering, University of Manchester, Manchester, UK;Department of Imaging Science and Biomedical Engineering, University of Manchester, Manchester, UK;Department of Imaging Science and Biomedical Engineering, University of Manchester, Manchester, UK
Venue:
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Year:
2005

Citing 2
Cited 6

Active Appearance Models

ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Image Segmentation by Shape Particle Filtering

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03

Cervical Vertebrae Tracking in Video-Fluoroscopy Using the Normalized Gradient Field

MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Robust autonomous model learning from 2D and 3D data sets

MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Improving the detection of osteoporosis from dental radiographs using active appearance models

ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Measurement error in statistical models of shape

Computer Methods and Programs in Biomedicine
Heterogeneous computing for vertebra detection and segmentation in x-ray images

Journal of Biomedical Imaging - Special issue on Parallel Computation in Medical Imaging Applications
Quantitative vertebral morphometry using neighbor-conditional shape models

MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

The shape and appearance of vertebrae on lateral dual x-ray absorptiometry (DXA) scans were statistically modelled. The spine was modelled by a sequence of overlapping triplets of vertebrae, using Active Appearance Models (AAMs). To automate vertebral morphometry, the sequence of trained models was matched to previously unseen scans. The dataset includes a significant number of pathologies. A new dynamic ordering algorithm was assessed for the model fitting sequence, using the best quality of fit achieved by multiple sub-model candidates. The accuracy of the search was improved by dynamically imposing the best quality candidate first. The results confirm the feasibility of substantially automating vertebral morphometry measurements even with fractures or noisy images.