Hierarchical active shape models, using the wavelet transform
Abstract
Active shape models (ASMs) are often limited by the inability of relatively few eigenvectors to capture the full range of biological shape variability. This paper presents a method that overcomes this limitation, by using a hierarchical formulation of active shape models, using the wavelet transform. The statistical properties of the wavelet transform of a deformable contour are analyzed via principal component analysis, and used as priors in the contour's deformation. Some of these priors reflect relatively global shape characteristics of the object boundaries, whereas, some of them capture local and high-frequency shape characteristics and, thus, serve as local smoothness constraints. This formulation achieves two objectives. First, it is robust when only a limited number of training samples is available. Second, by using local statistics as smoothness constraints, it eliminates the need for adopting ad hoc physical models, such as elasticity or other smoothness models, which do not necessarily reflect true biological variability. Examples on magnetic resonance images of the corpus callosum and hand contours demonstrate that good and fully automated segmentations can be achieved, even with as few as five training samples.
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- Authors
- Davatzikos, C., Tao, X., & Shen, D.
- Affiliations
- University of Pennsylvania (US)
- Johns Hopkins University (US)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2003-03
- Volume
- 22
- Issue
- 3
- Pages
- 414-423
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2142152601
- Cited by
- 215
- References
- 28
- Citations by year
- 2026: 1 · 2023: 2 · 2022: 4 · 2021: 5 · 2020: 5 · 2019: 4 · 2018: 3 · 2017: 7 · 2016: 13 · 2015: 10 · 2014: 21 · 2013: 13 · 2012: 11
- FWCI
- 14.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute on Aging (R01AG014971, R01 AG14971-03)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- active shape model, wavelet transform, deformable contours, shape variation, smoothness constraint, local statistics, shape priors, automatic segmentation, corpus callosum segmentation, magnetic resonance imaging, limited training data
- MeSH terms
- Algorithms, Corpus Callosum, Elasticity, Hand, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Motion, Pattern Recognition, Automated, Anatomy, Cross-Sectional
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