Viewpoint manifolds for action recognition
Journal on Image and Video Processing - Special issue on video-based modeling, analysis, and recognition of human motion
On the Manifold Structure of the Space of Brain Images
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Manifold learning for patient position detection in MRI
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Manifold learning for image-based breathing gating with application to 4D ultrasound
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Combining automated and interactive visual analysis of biomechanical motion data
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Foundations and Trends® in Computer Graphics and Vision
Spaces and manifolds of shapes in computer vision: An overview
Image and Vision Computing
Hierarchical manifold learning
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Sparse projections of medical images onto manifolds
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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We develop theory and algorithms to incorporate image manifold constraints in a level set segmentation algorithm. This provides a framework to simultaneously segment every image of data sets that vary due to two degrees of freedom - such as cardiopulmonary MR images which deform due to patient breathing and heartbeats. We derive two formulations: a 4D level set which loosely couples the level set function between neighbors in the 2D image manifold and a multilayer level set function which uses different levels of the level set function to represent shapes that shrink or grow. We characterize the set of shape manifolds that the multilayer level set function can represent, and derive the evolution equations for both frameworks. We offer results of segmenting the left ventricle in cardiopulmonary MRI; by automatically discovering the 2D manifold structure of the image set then simultaneously segmenting every frame. Both extensions improve on frame-by-frame approaches, and a comparison of the results offers insight into their strengths and weaknesses.