Multiscale 3-d shape representation and segmentation using spherical wavelets
D Nain, S Haker, A Bobick… - IEEE Transactions on …, 2007 - ieeexplore.ieee.org
This paper presents a novel multiscale shape representation and segmentation algorithm
based on the spherical wavelet transform. This work is motivated by the need to compactly …
based on the spherical wavelet transform. This work is motivated by the need to compactly …
[HTML][HTML] Learning continuous shape priors from sparse data with neural implicit functions
Statistical shape models are an essential tool for various tasks in medical image analysis,
including shape generation, reconstruction and classification. Shape models are learned …
including shape generation, reconstruction and classification. Shape models are learned …
Towards robust and effective shape modeling: Sparse shape composition
Organ shape plays an important role in various clinical practices, eg, diagnosis, surgical
planning and treatment evaluation. It is usually derived from low level appearance cues in …
planning and treatment evaluation. It is usually derived from low level appearance cues in …
Shape-driven 3D segmentation using spherical wavelets
D Nain, S Haker, A Bobick, A Tannenbaum - … 6, 2006. Proceedings, Part I 9, 2006 - Springer
This paper presents a novel active surface segmentation algorithm using a multiscale shape
representation and prior. We define a parametric model of a surface using spherical wavelet …
representation and prior. We define a parametric model of a surface using spherical wavelet …
Cortical surface shape analysis based on spherical wavelets
In vivo quantification of neuroanatomical shape variations is possible due to recent
advances in medical imaging and has proven useful in the study of neuropathology and …
advances in medical imaging and has proven useful in the study of neuropathology and …
Automated segmentation and shape characterization of volumetric data
VL Galinsky, LR Frank - NeuroImage, 2014 - Elsevier
Abstract Characterization of complex shapes embedded within volumetric data is an
important step in a wide range of applications. Standard approaches to this problem employ …
important step in a wide range of applications. Standard approaches to this problem employ …
Building 3-D statistical shape models by direct optimization
RH Davies, CJ Twining, TF Cootes… - IEEE Transactions on …, 2009 - ieeexplore.ieee.org
Statistical shape models are powerful tools for image interpretation and shape analysis. A
simple, yet effective, way of building such models is to capture the statistics of sampled point …
simple, yet effective, way of building such models is to capture the statistics of sampled point …
Hierarchical statistical shape models of multiobject anatomical structures: application to brain MRI
JJ Cerrolaza, A Villanueva… - IEEE Transactions on …, 2011 - ieeexplore.ieee.org
The accurate segmentation of subcortical brain structures in magnetic resonance (MR)
images is of crucial importance in the interdisciplinary field of medical imaging. Although …
images is of crucial importance in the interdisciplinary field of medical imaging. Although …
Sparse shape composition: A new framework for shape prior modeling
Image appearance cues are often used to derive object shapes, which is usually one of the
key steps of image understanding tasks. However, when image appearance cues are weak …
key steps of image understanding tasks. However, when image appearance cues are weak …
DeepSSM: a deep learning framework for statistical shape modeling from raw images
Statistical shape modeling is an important tool to characterize variation in anatomical
morphology. Typical shapes of interest are measured using 3D imaging and a subsequent …
morphology. Typical shapes of interest are measured using 3D imaging and a subsequent …