A Novel Algorithm for Fitting 3-D Active Appearance Models: Applications to Cardiac MRI Segmentation
We present an efficient algorithm for fitting three dimensional (3-D) Active Appearance Models (AAMs). We do so, by introducing a 3-D extension of a recently proposed method that is based on the inverse compositional image alignment algorithm. We demonstrate its applicability for the segmentation of the left ventricle in short axis cardiac MRI. We perform experiments to evaluate the speed and segmentation accuracy of our algorithm on a total of 1473 cardiac MR images acquired from 11 patients. The fitting is around 60 times faster than standard Gauss-Newton optimization, with a segmentation accuracy that is as good as, and often better than Gauss-Newton.
KeywordsLeft Ventricle Segmentation Accuracy Active Appearance Model Image Alignment Appearance Variation
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