A recursive filter for phase velocity assisted shape-based tracking of cardiac non-rigid motion

McEachen, John; Meyer, F.G.; Constable, R. Todd; Nehorai, Arye; Duncan, James S.

doi:10.1109/iccv.1995.466876

Cited by 17 publications

(15 citation statements)

References 19 publications

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“…Reasonably, we adopt the traditional image gradient magnitude as a component of the boundary energy module in Eq. (20). The boundary-based energy module is defined as E bnd (v(x(s), y(s))) = −|∇I (x(s), y(s))| 2 ds.…”

Section: Boundary-based Modulementioning

confidence: 99%

“…(20), the active contour model consists of three main energy modules: the boundary-based energy module, region-based energy module and contour self-constraint energy module.…”

Section: Parameter Initialization Of Em By K-means Algorithmmentioning

confidence: 99%

“…Another kind of method is to extract the motion by pixel intensity analysis of the apparent motion over the region [17,18]. Usually spatio-temporal gradients are estimated as motion information [19][20][21]. The advantages of these models are that they are robust and do not need a specific required initial step, because they make use of the information from the whole region.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, it has been gaining popularity in clinical examination in the past decade. Anal EUS, which owns a high frequency transducer (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) for detailed resolution, near field focusing, is the one to examine the anorectal tract [1].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A region and gradient based active contour model and its application in boundary tracking on anal canal ultrasound images

Xiao

Tsang

et al. 2007

Pattern Recognition

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Section: Boundary-based Modulementioning

confidence: 99%

“…(20), the active contour model consists of three main energy modules: the boundary-based energy module, region-based energy module and contour self-constraint energy module.…”

Section: Parameter Initialization Of Em By K-means Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A region and gradient based active contour model and its application in boundary tracking on anal canal ultrasound images

Xiao

Tsang

et al. 2007

Pattern Recognition

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“…Historically, cardiac motion analysis, especially of the LV, has been the main focus of such approaches. Generic deformable surface models [117]- [119], models exploiting curvature information [120]- [123], 4-D models [124], or models with temporal constraints [125], [126] have been previously described for cardiac motion analysis, and they are all based on surface tracking.…”

Section: Discussionmentioning

confidence: 99%

Estimating motion from MRI data

2003

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Invited Paper-Magnetic resonance imaging (MRI) is an ideal imaging modality to measure blood flow and tissue motion. It provides excellent contrast between soft tissues, and images can be acquired at positions and orientations freely defined by the user. From a temporal sequence of MR images, boundaries and edges of tissues can be tracked by image processing techniques. Additionally, MRI permits the source of the image signal to be manipulated. For example, temporary magnetic tags displaying a pattern of variable brightness may be placed in the object using MR saturation techniques, giving the user a known pattern to detect for motion tracking. The MRI signal is a modulated complex quantity, being derived from a rotating magnetic field in the form of an induced current. Well-defined patterns can also be introduced into the phase of the magnetization, and could be thought of as generalized tags. If the phase of each pixel is preserved during image reconstruction, relative phase shifts can be used to directly encode displacement, velocity and acceleration. New methods for modeling motion fields from MRI have now found application in cardiovascular and other soft tissue imaging. In this review, we shall describe the methods used for encoding, imaging, and modeling motion fields with MRI.

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A fast and highly automated approach to myocardial motion analysis using phase contrast magnetic resonance imaging

Bergvall¹,

Cain

Arheden

et al. 2006

Magnetic Resonance Imaging

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Purpose: To develop a fast and highly automated method for calculating two-dimensional myocardial motion and deformation using velocity encoded magnetic resonance imaging. Materials and Methods:Two-dimensional phase contrast magnetic resonance imaging was used to acquire time resolved velocity maps of the myocardium. Cardiac motion was calculated by an iterative integration-regularization scheme of low computational cost. Image segmentation was performed using active appearance models.Results: Validation of motion tracking was performed in N ϭ 47 subjects using saturation grid-tagging and closely followed "tag-lines." Image segmentation was validated vs. manual delineation. Conclusion:The speed and limited user interaction gives the method good potential for use in clinical practice.

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A recursive filter for phase velocity assisted shape-based tracking of cardiac non-rigid motion

Abstract: A framework for tracking pointwise periodic non-rigid motion of the heart's left ventricular (LV)

Cited by 17 publications

References 19 publications

A region and gradient based active contour model and its application in boundary tracking on anal canal ultrasound images

A region and gradient based active contour model and its application in boundary tracking on anal canal ultrasound images

Estimating motion from MRI data

A fast and highly automated approach to myocardial motion analysis using phase contrast magnetic resonance imaging

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