Projects:ParticlesForShapesAndComplexes
Back to NA-MIC Collaborations, Utah Algorithms
Shape Modeling and Analysis with Particle Systems
Overview
This work addresses technical challenges in biomedical shape analysis through the development of novel modeling and analysis methodologies, and seeks validation of those methodologies by their application to real-world research problems. The main focus of the work is the development and validation of a new framework for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The proposed optimization uses an entropy-based minimization that balances the simplicity of the model (compactness) with the accuracy of the surface representations. This framework is easily extended to handle more general classes of shape modeling, such as multiple-object complexes and correspondence based on functions of position. This work also addresses the issue of how to do hypothesis testing with the proposed modeling framework, since, to date, the shape analysis community has not reached a consensus regarding a systematic approach to statistical analysis with point-based models. Finally, another important issue that remains is how to visualize significant shape differences in a way that allows researchers to understand not only whether differences exist, but what those shape differences are. This latter consideration is obviously of importance in in relating shape differences to scientific hypotheses.
The following list is a summary of research and development results to date.
- We have implemented a mathematical framework and a robust numerical algorithm implementation for computing optimized correspondence-point shape models using an entropy-based optimization and particle-system technology.
- We have proposed a general methodology for hypothesis testing with point-based shape models that is suitable for use with the particle-based correspondence algorithm. Additionally, we have proposed ideas for visualization to aid in the interpretation of these shape statistics.
- The particle-based correspondence algorithm and statistical analysis methodology have been extended to more general classes of shape analysis problems: (a) the analysis of multiple-object complexes and (b) the generalization to correspondences based on generic functions of position.
- In cooperation with scientists and clinicians, we have published several papers that evaluate the above methodologies in the context of biomedical research.
Particle-Based Correspondence Optimization
The proposed correspondence optimization method is to construct a point-based sampling of a shape ensemble that simultaneously maximizes both the geometric accuracy and the statistical simplicity of the model. Surface point samples, which also define the shape-to-shape correspondences, are modeled as sets of dynamic particles that are constrained to lie on a set of implicit surfaces. Sample positions are optimized by gradient descent on an energy function that balances the negative entropy of the distribution on each shape with the positive entropy of the ensemble of shapes. We have also extended the method with a curvature-adaptive sampling strategy in order to better approximate the geometry of the objects.
Any set of implicitly defined surfaces, such as a set of binary segmentations, is appropriate as input to this framework. A binary mask, for example, contains an implicit shape surface at the interface of the labeled pixels and the background. Because the proposed method is completely generalizable to higher dimensions, we are able to process shapes in 2D and 3D using the same C++ software implementation, which is templated by dimension. Processing time on a 2GHz desktop machine scales linearly with the number of particles in the system and ranges from 20 minutes for a 2D system of a few thousand particles to several hours for a 3D system of tens of thousands of particles.
We have found that the proposed particle method compares favorably to state-of-the-art methods for optimizing surface point correspondences. Figure 1, for example, illustrates an experiment on a set hand shape contours that shows a favorable comparison with the Minimum Description Length (MDL) method of Davies, et al. [R Davies, et al., IEEE Trans Med Imaging, V. 21, No. 5, 2002]. In the figure, we also compare our results with a set of ideal, manually selected correspondences, which introduce anatomical knowledge of the digits. Principal component analysis shows that the proposed particle method and the manually selected points both produce very similar models, while the MDL model differs significantly.
Existing 3D MDL implementations rely on spherical parameterizations, and are therefore only capable of analyzing shapes topologically equivalent to a sphere. The particle-based method does not have this limitation. As a demonstration of this, we applied the proposed method to a set of 25 randomly chosen tori, drawn from a 2D, normal distribution parameterized by the small radius r and the large radius R. An analysis of the resulting model showed that the particle system method discovered the two pure modes of variation, with only 0.08% leakage into smaller modes.
Further preliminary experiments on 2D synthetic data and 3D medical image data (hippocampus shapes) are described in [1] and [2], but are omitted here for brevity. Figure 2 is taken from this work, for example, and illustrates two principal components of a model of the hippocampus.
Key Investigators
Josh Cates, Tom Fletcher, Ross Whitaker
Publications
In Print
