Learning flexible models from image sequences
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
An Efficient Method for Constructing Optimal Statistical Shape Models
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Measures for Benchmarking of Automatic Correspondence Algorithms
Journal of Mathematical Imaging and Vision
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The purpose of this study was to develop an automated method for the segmentation of the heart in a 3-D cardiac scintigram. This is immediately useful for eliminating a manual step in a previous version of a decision support system. The automatic segmentation method uses a statistical 3D-model, inspired by Active Shape, which locates the base and apex automatically from a cardiac scintigram. Key features of this algorithm are that it can handle cases where there is no or very little activity in the apex and also if there are additional parts of the heart where there is little activity. The algorithm has been tested on approximately 2000 cardiac scintigrams.