Paper
14 March 2011 A joint model for boundaries of multiple anatomical parts
Grégoire Kerr, Sebastian Kurtek, Anuj Srivastava
Author Affiliations +
Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 796246 (2011) https://doi.org/10.1117/12.877694
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States
Abstract
The use of joint shape analysis of multiple anatomical parts is a promising area of research with applications in medical diagnostics, growth evaluations, and disease characterizations. In this paper, we consider several features (shapes, orientations, scales, and locations) associated with anatomical parts and develop probability models that capture interactions between these features and across objects. The shape component is based on elastic shape analysis of continuous boundary curves. The proposed model is a second order model that considers principal coefficients in tangent spaces of joint manifolds as multivariate normal random variables. Additionally, it models interactions across objects using area-interaction processes. Using given observations of four anatomical parts: caudate, hippocampus, putamen and thalamus, on one side of the brain, we first estimate the model parameters and then generate random samples from them using the Metropolis-Hastings algorithm. The plausibility of these random samples validates the proposed models.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Grégoire Kerr, Sebastian Kurtek, and Anuj Srivastava "A joint model for boundaries of multiple anatomical parts", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 796246 (14 March 2011); https://doi.org/10.1117/12.877694
Lens.org Logo
CITATIONS
Cited by 3 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Shape analysis

Statistical modeling

Brain

Statistical analysis

Adaptive optics

Thalamus

Stochastic processes

Back to Top