A Multi-scale Feature Based Optic Flow Method for 3D Cardiac Motion Estimation

Becciu, A.; Assen, Hans C van; Florack, Luc; Kozerke, Sebastian; Roode, Vivian; Romeny, Bart M. Haar

doi:10.1007/978-3-642-02256-2_49

Cited by 14 publications

(7 citation statements)

References 26 publications

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“…Hence,

boldV (t) = (\begin{matrix} u (t) \\ v (t) \\ w (t) \end{matrix}) = - H {(boldx (t), s, t)}^{- 1} \frac{\partial (\nabla L {(boldx (t), s, t)}^{T})}{\partial t},

where H represents the spatial Hessian matrix of image L . In the literature, similar optic flow approaches that calculate velocity estimation at feature point location using the Hessian matrix are discussed in [9, 10, 43, 45]. …”

Section: Methodsmentioning

confidence: 99%

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3D Winding Number: Theory and Application to Medical Imaging

Becciu

Fuster

Pottek

et al. 2011

International Journal of Biomedical Imaging

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We develop a new formulation, mathematically elegant, to detect critical points of 3D scalar images. It is based on a topological number, which is the generalization to three dimensions of the 2D winding number. We illustrate our method by considering three different biomedical applications, namely, detection and counting of ovarian follicles and neuronal cells and estimation of cardiac motion from tagged MR images. Qualitative and quantitative evaluation emphasizes the reliability of the results.

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“…Hence,

boldV (t) = (\begin{matrix} u (t) \\ v (t) \\ w (t) \end{matrix}) = - H {(boldx (t), s, t)}^{- 1} \frac{\partial (\nabla L {(boldx (t), s, t)}^{T})}{\partial t},

Section: Methodsmentioning

confidence: 99%

“…This optic flow technique is not based on brightness conservation; therefore, it can be robustly applied directly on tagged MRI. In [ 43 ], a similar 3D motion estimation procedure has been presented. In this case, the critical points have been extracted by a methodology based on zerocrossings.…”

Section: Methodsmentioning

confidence: 99%

3D Winding Number: Theory and Application to Medical Imaging

Becciu

Fuster

Pottek

et al. 2011

International Journal of Biomedical Imaging

Self Cite

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show abstract

“…On the data set, proposed algorithm showed 100% and 83% of accuracy in recognizing anomalous events at two different locations. Alessandro Becciu, Luc Florack, Hans van Assen, Vivian Roode, Sebastian Kozerke, and Bart M, in [13] proposed a new three-dimensional (3D) multiscale OFbased method for analyzing a true 3D cardiac motion at voxel precision. Rizwan Chaudhry, Gregory Hager, Ren´e Vidal and Avinash Ravichandran, in [14] proposed usage of generalization of the Binet-Cauchy kernels to nonlinear dynamical systems (NLDS) and represented each frame of a video using a histogram of oriented optical flow (HOOF) to recognize human actions by classifying HOOF time-series.…”

Section: Related Workmentioning

confidence: 99%

Facilitating the Detection of ASD in Ultrasound Video using RHOOF and SVM

Annadate¹,

Nagmode²

2020

IJACSA

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In the medical field various motion tracking techniques like block matching, optical flow, and histogram of oriented optical flow (HOOF) are being experimented for the abnormality detection. The information furnished by the existing techniques is inadequate for medical diagnosis. This technique has an inherent drawback, as the entire image is considered for motion vector calculation, increasing the time complexity. Also, the motion vectors of unwanted objects are getting accounted during abnormality detection, leading to misidentification / misdiagnosis. In this research, our main objective is to focus more on the region of abnormality by avoiding the unwanted motion vectors from the rest of the portion of the heart, allowing better time complexity. Proposed a region-based HOOF (RHOOF) for blood motion tracking and estimation; after experimentation, it is observed that RHOOF is four times faster than HOOF. The performance of supervised machine learning techniques was evaluated based on accuracy, precision, sensitivity, specificity, and area under the curve. In the medical field more importance is given to the sensitivity than accuracy. Support vector machine (SVM) has outperformed other technique on sensitivity and time complexity, hence chosen for abnormality classification in this work. An algorithm has been devised to use combination of RHOOF and SVM for the detection of atrial septal defect (ASD).

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“…By design, these methods discard the remaining motion information and solely rely on the information conveyed by shift invariant intensity patterns. In contrast, motion-based methods explicitly model the existing misalignment as local motions and use the variation in motion as cues for registration [8] and the existence of an underlying cause [10].…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis of three‐dimensional optical flow separability in volumetric images

Hadian-Jazi

Bab–Hadiashar

Hoseinnezhad

2015

IET Computer Vision

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Three‐dimensional (3D) motion analysis of dynamic body organs using volumetric images is of increasing interest in different computer vision applications. A number of methods for estimation of 3D optical flow in those images have been developed in recent years. However, theoretical limits of 3D optical flow‐based motion estimation and segmentation are yet to be analysed. In this study, a statistical analysis of 3D optical flow is presented and the results are used to predict the separability of local 3D motions. Experimental results, using both synthetic and real images, demonstrate the applicability of the proposed analysis to predict the separability of two motions in terms of the parameters quantifying their relative motion and the scale of measurement noise.

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A Multi-scale Feature Based Optic Flow Method for 3D Cardiac Motion Estimation

Cited by 14 publications

References 26 publications

3D Winding Number: Theory and Application to Medical Imaging

3D Winding Number: Theory and Application to Medical Imaging

Facilitating the Detection of ASD in Ultrasound Video using RHOOF and SVM

Statistical analysis of three‐dimensional optical flow separability in volumetric images

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