A visual framework for the study of cardiac motion

Demi, M.; Calamai, R.; Coppini, Giuseppe; Valli, G.

doi:10.1109/cic.1990.144302

Cited by 12 publications

(8 citation statements)

References 3 publications

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“…A possible 3D expansion is suggested taking similar considerations in to account [16]. However, similar work of Demi et al [17] implements regularization of the flow vectors using an interpolation scheme to estimate the shape correspondence between subsampled shape features. Experimental methods using physically implanted markers to quantify the motion in animal heart are presented in [18] where the markers were corresponded and tracked in a stereo static external co-ordinate system.…”

Section: Shape Correspondence Based Motion Estimationmentioning

confidence: 99%

Fuzzy shape based motion evaluation of left ventricle using genetic algorithm

Mishra

Dutta

Ghosh

2006

Image and Vision Computing

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Section: Shape Correspondence Based Motion Estimationmentioning

confidence: 99%

Fuzzy shape based motion evaluation of left ventricle using genetic algorithm

Mishra

Dutta

Ghosh

2006

Image and Vision Computing

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“…Additional efforts in 2-D tracking related to the frame-toframe approach have been proposed [12]- [14]. The first two approaches differ in the term used for regularization of the flow vectors.…”

Section: B Related Efforts In Image Sequence Analysismentioning

confidence: 99%

“…The first two approaches differ in the term used for regularization of the flow vectors. Additionally, [12] uses an interpolation scheme to quantify shape between subsampled shape features. The work in [13] also optimizes a cost functional over the space of the contour, however the resulting minimization is highly nonlinear and significantly more complex due to the requirement for mapping shape-based contour correspondences back into Euclidean space values to compute the smoothing term of the functional.…”

Section: B Related Efforts In Image Sequence Analysismentioning

confidence: 99%

“…The difference between our observed values, , and the estimated mappings, , in (10) is the observation error, , hence the index of performance we wish to minimize, , is defined as follows: (11) where remains the . The optimum value of for which the performance index, , attains its minimum value is obtained by solving the classic least squares relationship (12) where the correlation matrix is of size or , depending on the temporal model ( or -is the number of temporal harmonics, the number of spatial harmonics).…”

Section: Adaptive Estimationmentioning

confidence: 99%

“…The final step in our RLS formulation is to develop a recursive equation for updating the least-squares estimate of at iteration . Using (12), (16), and (17) the new estimate of the state vector is formed (19) Fig. 5.…”

Section: Adaptive Estimationmentioning

confidence: 99%

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Multiframe temporal estimation of cardiac nonrigid motion

McEachen

Nehorai

Duncan

2000

IEEE Trans. on Image Process.

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A robust, flexible system for tracking the point to point nonrigid motion of the left ventricular (LV) endocardial wall in image sequences has been developed. This system is unique in its ability to model motion trajectories across multiple frames. The foundation of this system is an adaptive transversal filter based on the recursive least-squares algorithm. This filter facilitates the integration of models for periodicity and proximal smoothness as appropriate using a contour-based description of the object's boundaries. A set of correspondences between contours and an associated set of correspondence quality measures comprise the input to the system. Frame-to-frame relationships from two different frames of reference are derived and analyzed using synthetic and actual images. Two multiframe temporal models, both based on a sum of sinusoids, are derived. Illustrative examples of the system's output are presented for quantitative analysis. Validation of the system is performed by comparing computed trajectory estimates with the trajectories of physical markers implanted in the LV wall. Sample case studies of marker trajectory comparisons are presented. Ensemble statistics from comparisons with 15 marker trajectories are acquired and analyzed. A multiframe temporal model without spatial periodicity constraints was determined to provide excellent performance with the least computational cost. A multiframe spatiotemporal model provided the best performance based on statistical standard deviation, although at significant computational expense.

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