On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Active shape models—their training and application
Computer Vision and Image Understanding
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Generic Probabilistic Active Shape Model for Organ Segmentation
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Visual Analytics for model-based medical image segmentation: Opportunities and challenges
Expert Systems with Applications: An International Journal
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The Active Shape Model (ASM) is a segmentation algorithm which uses a Statistical Shape Model (SSM) to constrain segmentations to 'plausible' shapes. This makes it possible to robustly segment organs with low contrast to adjacent structures. The standard SSM assumes that shapes are Gaussian distributed, which implies that unseen shapes can be expressed by linear combinations of the training shapes. Although this assumption does not always hold true, and several nonlinear SSMs have been proposed in the literature, virtually all applications in medical imaging use the linear SSM. In this work, we investigate 3D ASM segmentation with a nonlinear SSM based on Kernel PCA. We show that a recently published energy minimization approach for constraining shapes with a linear shape model extends to the nonlinear case, and overcomes shortcomings of previously published approaches. Our approach for nonlinear ASM segmentation is applied to vertebra segmentation and evaluated against the linear model.