Active shape models—their training and application
Computer Vision and Image Understanding
An Adaptive-Focus Deformable Model Using Statistical and Geometric Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Automatic Construction of 3D Statistical Deformation Models Using Non-rigid Registration
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Competitive Mixture of Deformable Models for Pattern Classification
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Boundary Finding with Correspondence Using Statistical Shape Models
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Conversion of complex contour line definitions into polygonal element mosaics
SIGGRAPH '78 Proceedings of the 5th annual conference on Computer graphics and interactive techniques
A Statistical Assembled Model for Segmentation of Entire 3D Vasculature
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
A new scheme for automated 3D PDM construction using deformable models
Image and Vision Computing
3D object segmentation using B-Surface
Image and Vision Computing
Iterative 3D point-set registration based on hierarchical vertex signature (HVS)
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Construction of a human topological model from medical data
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Medical Image Segmentation Using Minimal Path Deformable Models With Implicit Shape Priors
IEEE Transactions on Information Technology in Biomedicine
Sparse representation of deformable 3D organs with spherical harmonics and structured dictionary
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
Using haptic and neural networks for surface and mechanical properties 3D reconstruction
UCAmI'12 Proceedings of the 6th international conference on Ubiquitous Computing and Ambient Intelligence
| Hi-index | 0.01 |
While active shape model (ASM) has been increasingly adopted in the medical domain, there are issues that need to be addressed for it to be applicable in practice. Among them, the small sample size problem and how to represent the variation of the clutter of surroundings are two of the challenges. In this paper, to overcome these problems, we propose a novel multi-resolution statistical deformable model and the associated techniques for the reconstruction of soft-tissue organs such as livers. To address the small sample size problem, we define a multi-resolution integrated model for soft-tissue organs called MISTO that is able to capture the most significant deformations from a small training set as well as to generate representative variation modes of the organ shapes. To deal with the complex surroundings of the model surface or landmark points in the underlying medical images during model deformation, we propose to apply multi-resolution appearance models which allows the surrounding visual context of the model surface points to be learnt and characterized automatically from the training samples. By combining the powerful shape models and the resulting context constraints, the object segmentation and reconstruction process can be carried out very robustly. Furthermore, to avoid the local minima during model optimization, we develop an adaptive deformation strategy such that the more stable parts of the surface are moved prior to the rest of the model surface. The experimental and validation results verify that our proposed approaches can be successfully and robustly applied to the reconstruction of the soft-tissue organs such as the human liver. The major contributions of our approaches are that we extend the traditional ASM to address open problems associated with reconstructing significantly deformable three-dimensional anatomies in cluttered surrounding, and we propose effective ways to formulate the perceptual knowledge of the anatomies and make use of it in the process of model construction and deformation for medical reconstruction.