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Abstract Segmentation methods based on pixel classification are powerful but often slow. We introduce two general algorithms, based on sparse classification, for optimizing the computation while still obtaining accurate segmentations. The computational costs of the algorithms are derived, and they are demonstrated on real 3-D magnetic resonance imaging and 2-D radiograph data. We show that […]

Abstract We present a novel method for automatic shape model building from a collection of training shapes. The result is a shape model consisting of the mean model and the major modes of variation with a dense correspondence map between individual shapes. The framework consists of iterations where a medial shape representation is deformed into […]

Abstract A core part of many medical image segmentation techniques is the point distribution model, i.e., the landmark-based statistical shape model which describes the type of shapes under consideration. To build a proper model, that is flexible and generalizes well, one typically needs a large amount of landmarked training data, which can be hard to […]