Title
A level set framework with a shape and motion prior for segmentation and region tracking in echocardiography.
Abstract
We describe a level set formulation using both shape and motion prior, for both segmentation and region tracking in high frame rate echocardiographic image sequences. The proposed approach uses the following steps: registration of the prior shape, level set segmentation constrained through the registered shape and region tracking. Registration of the prior shape is expressed as a rigid or an affine transform problem, where the transform minimizing a global region-based criterion is sought. This criterion is based on image statistics and on the available estimated axial motion data. The segmentation step is then formulated through front propagation, constrained with the registered shape prior. The same region-based criterion is used both for the registration and the segmentation step. Region tracking is based on the motion field estimated from the interframe level set evolution. The proposed approach is applied to high frame rate echocardiographic sequences acquired in vivo. In this particular application, the prior shape is provided by a medical expert and the rigid transform is used for registration. It is shown that this approach provides consistent results in terms of segmentation and stability through the cardiac cycle. In particular, a comparison indicates that the results provided by our approach are very close to the results obtained with manual tracking performed by an expert cardiologist on a Doppler Tissue Imaging (DTI) study. These preliminary results show the ability of the method to perform region tracking and its potential for dynamic parametric imaging of the heart.
Year
DOI
Venue
2006
10.1016/j.media.2005.06.004
Medical Image Analysis
Keywords
Field
DocType
Level set,Segmentation,Registration,Shape prior,Tracking,Echography,Cardiac ultrasound,Heart,Motion prior
Affine transformation,Computer vision,Motion field,Scale-space segmentation,Pattern recognition,Segmentation,Level set,Parametric statistics,Frame rate,Artificial intelligence,Inter frame,Mathematics
Journal
Volume
Issue
ISSN
10
2
1361-8415
Citations 
PageRank 
References 
21
2.03
17
Authors
6
Name
Order
Citations
PageRank
Igor Dydenko1414.17
Fadi Jamal2212.03
Olivier Bernard369063.59
Jan D'hooge4424.73
Isabelle E. Magnin554064.87
Denis Friboulet640332.65