Segmentation Informed by Manifold Learning

  • Qilong Zhang
  • Richard Souvenir
  • Robert Pless
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3757)


In many biomedical imaging applications, video sequences are captured with low resolution and low contrast challenging conditions in which to detect, segment, or track features. When image deformations have just a few underlying causes, such as continuously captured cardiac MRI without breath-holds or gating, the captured images lie on a low-dimensional, non-linear manifold. The manifold structure of such image sets can be extracted by automated methods for manifold learning. Furthermore, the manifold structure of these images offers new constraints for tracking and segmentation of relevant image regions. We illustrate how to incorporate these new constraints within a snake-based energy minimization approach, and demonstrate improvements in using snakes to segment a set of cardiac MRI images in challenging conditions.


Independent Component Analysis Active Contour Model Manifold Structure Manifold Learn Smoothness Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Qilong Zhang
    • 1
  • Richard Souvenir
    • 1
  • Robert Pless
    • 1
  1. 1.Department of Computer Science and EngineeringWashington UniversitySt. LouisUSA

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