Results 1 
5 of
5
Pointtracked quantitative analysis of left ventricular motion from 3D image sequences
 IEEE Transactions on Medical Imaging
, 2000
"... We propose and validate the hypothesis that we can use differential shape properties of the myocardial surfaces to recover dense field motion from standard three–dimensional image data (MRI and CT). Quantitative measures of left ventricular regional function can be further inferred from the point c ..."
Abstract

Cited by 32 (15 self)
 Add to MetaCart
(Show Context)
We propose and validate the hypothesis that we can use differential shape properties of the myocardial surfaces to recover dense field motion from standard three–dimensional image data (MRI and CT). Quantitative measures of left ventricular regional function can be further inferred from the point correspondence maps. The noninvasive, algorithm–derived results are validated in two levels: the motion trajectories are compared to those of implanted imaging–opaque markers of a canine model in two imaging modalities, where sub–pixel accuracy is achieved, and the validity of using motion parameters (path length and thickness changes) in detecting myocardial injury is tested by comparison to post–mortem TTC staining of myocardial tissue, where the Pearson product–moment correlation value is 0.968.
Superquadric Segmentation in Range Images via Fusion of Region and Boundary Information
, 2007
"... The high potential of Superquadrics as modeling elements for image segmentation tasks has been pointed out since years in the computer vision community. In this work we employ superquadrics as modeling elements for multiple object segmentation in range images. Segmentation is executed in two stages. ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
(Show Context)
The high potential of Superquadrics as modeling elements for image segmentation tasks has been pointed out since years in the computer vision community. In this work we employ superquadrics as modeling elements for multiple object segmentation in range images. Segmentation is executed in two stages. Firstly, a hypothesis about the values of the segmentation parameters is generated. Secondly, the hypothesis is refined locally. In both stages, object boundary and region information are considered. Boundary information is derived via modelbased edge detection in the input range image. Hypothesis generation uses boundary information to isolate image regions which can be accurately described by superquadrics. Within hypothesis refinement, a gametheoretic framework is used to fuse the two information sources by associating an objective function to each information source. Iterative optimization of the two objective functions in succession, outputs a precise description of all image objects. We demonstrate experimentally, that this approach substantially improves the most established method in superquadric segmentation, in terms of accuracy and computational efficiency. We demonstrate the applicability of our segmentation framework in real world applications by constructing a novel robotic system for automatic unloading of jumbled boxlike objects from platforms. Index Terms I.4.8.g range data, I.4.8.j shape, I.4.9 applications, I.4.7.e size and shape, I.4.6.e region growing, partitioning, I.4.6.a edge and feature detection, I.4.8.l surface fitting. I.
Volumetric Layer Segmentation Using a Generic Shape Constraint with Applications to Cortical Shape Analysis
, 2000
"... A novel approach has been developed in this thesis for the problem of segmenting volumetric layers, a type of structure often encountered in medical image analysis. This approach is aimed towards the use of structural information to enhance the performance of the segmentation process. While some org ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
A novel approach has been developed in this thesis for the problem of segmenting volumetric layers, a type of structure often encountered in medical image analysis. This approach is aimed towards the use of structural information to enhance the performance of the segmentation process. While some organs have more consistent global shape and can be characterized using a specific shape model, other anatomical structures possess much more complex shape with possibly high variability which needs a more generic shape constraint. The threedimensional (3D) nature of anatomical structures necessitates the use of volumetric approaches that exploit complete spatial information and therefore are far superior to the nonoptimal and oftenbiased 2D methods. Our method takes a volumetric approach, and incorporates a generic shape constraint – in particular, a thickness constraint. The resulting coupled surfaces algorithm with a level set implementation not only offers segmentation with the advantages of minimal user interaction, robustness to initialization and computational efficiency, but also facilitates the extraction and measurement of many geometric features of the volumetric layer. The algorithm
Resonance, Ultrasound, and XRay CT images. Estimation of 3D Left Ventricular Deformation from Medical Images Using Biomechanical Models
, 2000
"... The noninvasive quantitative estimation of regional cardiac deformation has important clinical implications for the assessment of viability in the heart wall. In this work we describe a general framework for estimating soft tissue deformation from sequences of threedimensional medical images. We a ..."
Abstract
 Add to MetaCart
(Show Context)
The noninvasive quantitative estimation of regional cardiac deformation has important clinical implications for the assessment of viability in the heart wall. In this work we describe a general framework for estimating soft tissue deformation from sequences of threedimensional medical images. We also explore some of their theoretical constraints which can be used to guide the selection of an appropriate model for the displacement field. We then apply this framework to the problem of estimating left ventricular deformations from sequences of 3D image sequences. The images are segmented interactively to extract the endocardial and epicardial surfaces. Then, initial frametoframe correspondences are established between points on the surfaces using a shapetracking approach. The myocardium is modeled using a transversely isotropic linear elastic model, which accounts for the preferential stiffness of the left ventricular myocardium along its fiber directions. The measurements and the model are integrated within a Bayesian estimation framework. The resulting equations are solved using the finite element method, to produce a dense displacement field for the whole of the left ventricle. The dense displacement field is, in turn, used to calculate the deformation of the heart wall in terms of the strains.
Abstract Estimation of 3D Left Ventricular Deformation from Medical Images Using Biomechanical Models.
, 2000
"... The noninvasive quantitative estimation of regional cardiac deformation has important clinical implications for the assessment of viability in the heart wall. In this work we describe a general framework for estimating soft tissue deformation from sequences of threedimensional medical images. We a ..."
Abstract
 Add to MetaCart
(Show Context)
The noninvasive quantitative estimation of regional cardiac deformation has important clinical implications for the assessment of viability in the heart wall. In this work we describe a general framework for estimating soft tissue deformation from sequences of threedimensional medical images. We also explore some of their theoretical constraints which can be used to guide the selection of an appropriate model for the displacement field. We then apply this framework to the problem of estimating left ventricular deformations from sequences of 3D image sequences. The images are segmented interactively to extract the endocardial and epicardial surfaces. Then, initial frametoframe correspondences are established between points on the surfaces using a shapetracking approach. The myocardium is modeled using a transversely isotropic linear elastic model, which accounts for the preferential stiffness of the left ventricular myocardium along its fiber directions. The measurements and the model are integrated within a Bayesian estimation framework. The resulting equations are solved using the finite element method, to produce a dense displacement field for the whole of the left ventricle. The dense displacement field is, in turn, used to calculate the deformation of the heart wall in terms of the strains. This method was tested on over 40 image sequences, and the strains produced using this noninvasive technique exhibit high correlation with strains simultaneously obtained from invasive measurements using implanted markers and sonomicrometers. We also demonstrate that these strains are useful as predictors of the viability of the underlying tissue and can be