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IPMI
2007
Springer

Shape Regression Machine

12 years 18 days ago
Shape Regression Machine
Abstract. We present a machine learning approach called shape regression machine (SRM) to segmenting in real time an anatomic structure that manifests a deformable shape in a medical image. Traditional shape segmentation methods rely on various assumptions. For instance, the deformable model assumes that edge defines the shape; the Mumford-Shah variational method assumes that the regions inside/outside the (closed) contour are homogenous in intensity; and the active appearance model assumes that shape/appearance variations are linear. In addition, they all need a good initialization. In contrast, SRM poses no such restrictions. It is a two-stage approach that leverages (a) the underlying medical context that defines the anatomic structure and (b) an annotated database that exemplifies the shape and appearance variations of the anatomy. In the first stage, it solves the initialization problem as object detection and derives a regression solution that needs just one scan in principle. In...
Shaohua Kevin Zhou, Dorin Comaniciu
Added 16 Nov 2009
Updated 16 Nov 2009
Type Conference
Year 2007
Where IPMI
Authors Shaohua Kevin Zhou, Dorin Comaniciu
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