Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Active shape models—their training and application
Computer Vision and Image Understanding
Parametrization of closed surfaces for 3-D shape description
Computer Vision and Image Understanding
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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"Active surfaces" or deformable models have been proposed for the segmentation of anatomic structures in MRI data. Such algorithms are dependent on a good initial approximation of the target shape. The purpose of this work was to develop a reliable method for automatic generation of a starting point for segmentation of the lateral ventricle. The algorithm uses a parametric representation of an average lateral ventricle, which is customized for each individual by modulating the parametric coefficients based on the brain parenchymal fraction. The method was developed with a training set of 6 healthy controls and 25 patients with multiple sclerosis, and tested on an additional set of 10 patients. Compared to the average ventricle, this new approach provided a closer approximation to the manually segmented ventricular shape in 81% of the cases in the training set and 100% of the additional test set.