Statistical analysis with missing data
Statistical analysis with missing data
Robust regression and outlier detection
Robust regression and outlier detection
Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
EM algorithms for PCA and SPCA
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Computational Statistics & Data Analysis
Nonlinear Shape Statistics in Mumford-Shah Based Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Non-rigid registration using distance functions
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Outlier identification in high dimensions
Computational Statistics & Data Analysis
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Statistical shape models have gained widespread use in medical image analysis. In order for such models to be statistically meaningful, a large number of data sets have to be included. The number of available data sets is usually limited and often the data is corrupted by imaging artifacts or missing information. We propose a method for building a statistical shape model from such "lousy" data sets. The method works by identifying the corrupted parts of a shape as statistical outliers and excluding these parts from the model. Only the parts of a shape that were identified as outliers are discarded, while all the intact parts are included in the model. The model building is then performed using the EM algorithm for probabilistic principal component analysis, which allows for a principled way to handle missing data. Our experiments on 2D synthetic and real 3D medical data sets confirm the feasibility of the approach. We show that it yields superior models compared to approaches using robust statistics, which only downweight the influence of outliers.