Small Sample Size Learning for Shape Analysis of Anatomical Structures

We present a novel approach to statistical shape analysis of anatomical structures based on small sample size learning techniques. The high complexity of shape models used in medical image analysis, combined with a typically small number of training examples, places the problem outside the realm of classical statistics. This difficulty is traditionally overcome by first reducing dimensionality of the shape representation (e.g., using PCA) and then performing training and classification in the reduced space defined by a few principal components. We propose to learn the shape differences between the classes in the original high dimensional parameter space, while controlling the capacity (generalization error) of the classifier. This approach makes significantly fewer assumptions on the properties and the distribution of the underlying data, which can be advantageous in anatomical shape analysis where little is known about the true nature of the input data. Support Vector Machines with Ra...

• Type: Conference
• Year: 2000
• Where: MICCAI
• Authors: Polina Golland, W. Eric L. Grimson, Martha Elizabeth Shenton, Ron Kikinis
• Keywords: Medical Imaging, MICCAI 2000, Shape Analysis, Small Sample Size, Statistical Shape Analysis