The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Statistical models of appearance for medical image analysis and computer vision:
Abstract
Statistical models of shape and appearance are powerful tools for interpreting medical images. We assume a training set of images in which corresponding landmark points have been marked on every image. From this data we can compute a statistical model of the shape variation, a model of the texture variation and a model of the correlations between shape and texture. With enough training examples such models should be able to synthesize any image of normal anatomy. By finding the parameters which optimize the match between a synthesized model image and a target image we can locate all the structures represented by the model. Two approaches to the matching will be described. The Active Shape Model essentially matches a model to boundaries in an image. The Active Appearance Model finds model parameters which synthesize a complete image which is as similar as possible to the target image. By using a difference decomposition approach the current difference between target image and synthesized model image can be used to update the model parameters, leading to rapid matching of complex models. We will demonstrate the application of such models to a variety of different problems.
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Cites
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Self-citationPrincipal warps: thin-plate splines and the decomposition of deformations
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Self-citationProcrustes Methods in the Statistical Analysis of Shape
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Cited by
A Bayesian model of shape and appearance for subcortical brain segmentation
Patenaude, B., Smith, S. M., Kennedy, D. N., & Jenkinson, M. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.02.046 · Cited by 2,287
MORPH: A Longitudinal Image Database of Normal Adult Age-Progression
Ricanek, K., & Tesafaye, T. (2006) · 7th International Conference on Automatic Face and Gesture Recognition (FGR06) · doi:10.1109/fgr.2006.78 · Cited by 778
A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit
Padilla, R., Passos, W. L., Dias, T. L. B., Netto, S. L., & da Silva, E. A. B. (2021) · Electronics · doi:10.3390/electronics10030279 · Cited by 735
Distributed deep learning across multisite datasets for generalized CT hemorrhage segmentation
Remedios, S. W., Roy, S., Bermudez, C., Patel, M. B., Butman, J. A., Landman, B. A., & Pham, D. L. (2020) · Medical Physics · doi:10.1002/mp.13880 · Cited by 37
Multi-object Segmentation of Head Bones
Kainmueller, D., Lamecker, H., Seim, H., & Zachow, S. (2009) · The MIDAS Journal · doi:10.54294/t2bzlq · Cited by 30
Metadata
- Authors
- Cootes, T. F., & Taylor, C. J.
- Venue
- SPIE Proceedings
- Publisher
- SPIE
- Published
- 2001-07-03
- Volume
- 4322
- Pages
- 236-248
- ISSN
- 0277-786X (ISSN-L)
- OpenAlex
- W2152826766
- Cited by
- 429
- References
- 35
- FWCI
- 31.00
- Type
- conference paper
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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