Subject specific shape modeling with incremental mixture models

Authors:
Lichao Wang;Karim Lekadir;Ismail EI-Hamamsy;Magdi Yacoub;Guang-Zhong Yang
Affiliations:
Institute of Biomedical Engineering, Imperial College London, UK;Institute of Biomedical Engineering, Imperial College London, UK;National Heart and Lung Institute, Imperial College London, UK;National Heart and Lung Institute, Imperial College London, UK;Institute of Biomedical Engineering, Imperial College London, UK
Venue:
MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
Year:
2010

Citing 6
Cited 0

Boundary Finding with Prior Shape and Smoothness Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Singular Value Decomposition of Uncertain Data with Missing Values

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Weighted and Robust Incremental Method for Subspace Learning

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Bidirectional Segmentation of Three-Dimensional Cardiac MR Images Using a Subject-Specific Dynamical Model

MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
A level set framework using a new incremental, robust Active Shape Model for object segmentation and tracking

Image and Vision Computing
Artificial enlargement of a training set for statistical shape models: application to cardiac images

FIMH'05 Proceedings of the Third international conference on Functional Imaging and Modeling of the Heart

Quantified Score

Hi-index	0.00

Visualization

Abstract

Statistical shape models provide versatile tools for incorporating statistical priors for image segmentation. Difficulties arise, however, when the target anatomical shape differs significantly from the training set used for model construction. This paper presents a novel approach for fast and accurate segmentation of subject-specific geometries based on models largely derived from normal subjects. This technique is particularly suitable for analyzing complex structures such as severely abnormal patient datasets. The proposed method uses online principal component update to incorporate subject-specific geometry. Mixture models are used to estimate the latent density distribution of the data, thus enabling adequate constraining during active shape propagation. Validation based on hypertrophic cardiomyopathy (HCM) datasets with MRI shows significant improvement in overall accuracy and increased adaptation to complex structures.