Order Statistic Based Cardiac Boundary Detection in 3D+t Echocardiograms

We propose a boundary detector for echocardiographic images to be used in conjunction with deformable models. It is well suited to detect endocardial and epicardial boundaries in both 2D and 3D images. We demonstrate its capabilities on an example of Active Shape Models, where it is used as a force driving the mesh towards the cardiac walls. Although the proposed approach is mostly specific to echocardiography, it does not require any training to learn the image appearance (since construction of a training set of echocardiograms is very difficult and error prone). The detector is based on computing the medians of a series of neighborhoods and analyzing the change in their values to look for the evidence of an edge. The proposed algorithm was tested on thirty 3D echocardiographic sequences (corresponding to 10 healthy and 10 dyssinchronous hearts, the latter imaged at two stages of cardiac resynchronization therapy: before and at twelve month followup).

[1]  Jøger Hansegård,et al.  Constrained Active Appearance Models for Segmentation of Triplane Echocardiograms , 2007, IEEE Transactions on Medical Imaging.

[2]  Hans-Peter Meinzer,et al.  Statistical shape models for 3D medical image segmentation: A review , 2009, Medical Image Anal..

[3]  Luigi Landini,et al.  Advanced Image Processing in Magnetic Resonance Imaging , 2005 .

[4]  Dorin Comaniciu,et al.  3D ultrasound tracking of the left ventricle using one-step forward prediction and data fusion of collaborative trackers , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Jasjit S. Suri,et al.  Handbook of Biomedical Image Analysis , 2005 .

[6]  C. Taylor,et al.  Active shape models - 'Smart Snakes'. , 1992 .

[7]  Alejandro F. Frangi,et al.  3D Active Shape and Appearance Models in Cardiac Image Analysis , 2006, Handbook of Mathematical Models in Computer Vision.

[8]  Alessandro Sarti,et al.  Left ventricular volume estimation for real-time three-dimensional echocardiography , 2002, IEEE Transactions on Medical Imaging.

[9]  A. Laine,et al.  Segmentation of real-time three-dimensional ultrasound for quantification of ventricular function: a clinical study on right and left ventricles. , 2005, Ultrasound in medicine & biology.

[10]  Elsa D. Angelini,et al.  State of the Art of Level Set Methods in Segmentation and Registration of Medical Imaging Modalities , 2005 .

[11]  Douglas L. Jones,et al.  Detection of lines and boundaries in speckle images-application to medical ultrasound , 1999, IEEE Transactions on Medical Imaging.

[12]  Alejandro F. Frangi,et al.  A statistical shape model of the heart and its application to model-based segmentation , 2007, SPIE Medical Imaging.

[13]  Carlos R. Castro-Pareja,et al.  Registration-assisted segmentation of real-time 3-D echocardiographic data using deformable models , 2005, IEEE Transactions on Medical Imaging.

[14]  Alan C. Bovik,et al.  Edge detection using median comparisons , 1986, Comput. Vis. Graph. Image Process..

[15]  Gregory G. Slabaugh,et al.  Information-Theoretic Feature Detection in Ultrasound Images , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Alejandro F. Frangi,et al.  A Survey of Three-Dimensional Modeling Techniques for Quantitative Functional Analysis of Cardiac Images , 2005 .

[17]  David C. Hoaglin,et al.  Some Implementations of the Boxplot , 1989 .

[18]  Victor Mor-Avi,et al.  Quantitative Assessment of Left Ventricular Size and Function: Side-by-Side Comparison of Real-Time Three-Dimensional Echocardiography and Computed Tomography With Magnetic Resonance Reference , 2006, Circulation.

[19]  J. Alison Noble,et al.  Ultrasound image segmentation: a survey , 2006, IEEE Transactions on Medical Imaging.