Automated septum thickness measurement—A Kalman filter approach

Snare, Sten Roar; Mjølstad, Ole Christian; Orderud, Fredrik; Dalen, Håvard; Torp, Hans

doi:10.1016/j.cmpb.2011.02.011

Cited by 6 publications

(6 citation statements)

References 17 publications

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“…Helpful tools implemented in the scanner have been tested and may help inexperienced users to assess for example, left ventricular function and left ventricular hypertrophy [30,31]. Algorithms for automatic detection of cardiac cycles have been implemented in the scanner [32].…”

Section: Future Perspectivementioning

confidence: 99%

Feasibility and clinical implementation of hand-held echocardiography

Dalen

Haugen

Graven

2013

Expert Review of Cardiovascular Therapy

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Echocardiography is essential in the evaluation of patients with suspected or known cardiovascular disease. The development of pocket-size hand-held echocardiographic devices, suitable for a quick assessment of cardiac structures and function, has been shown to improve diagnostics and patient workflow. In the hands of experts, pocket-size machines capable of gray tone and color flow imaging offer high accuracy for the assessment of ventricular function, the detection of pericardial and pleural effusions and are suitable for semiquantitative evaluation of valvular function. The machines are also suitable for noncardiac imaging. Point-of-care echocardiography with pocket-size hand-held echocardiographic devices should be an integrated part of the physical examination of patients in many situations and will probably be performed by an increasing number of both experts and nonexperts as well. However, the benefit relies on the level of competence of the users and tailored training is needed to answer specific questions.

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Section: Future Perspectivementioning

confidence: 99%

Feasibility and clinical implementation of hand-held echocardiography

Dalen

Haugen

Graven

2013

Expert Review of Cardiovascular Therapy

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“…Descriptions of the parameters in (6) to (10) are provided in Table 2, and P − k and P k are the covariance matrices for the estimation errors ofx − k andx k , respectively. For each update process, the state vectorx k and covariance matrix P k are updated.…”

Section: Kalman Updatementioning

confidence: 99%

“…• Central position (x, y, z) of the skeletal structure -for 0 < θ < 3π A total of 300 synthetic slices were generated along the z-axis of the synthetic model for every inter z in [10,309]. To measure the performances of the proposed and CV models in a noisy environment, Gaussian noises, ranging from 0 to 20 db with 5 db offset, were added to the synthetic slices.…”

Section: Synthetic Vessel Modelmentioning

confidence: 99%

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Adaptive Kalman snake for semi-autonomous 3D vessel tracking

Lee

2015

Computer Methods and Programs in Biomedicine

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“…Alternatively, one could consider intensity/contrast dependent terms for w to down-select points to learn relative velocities. Note that in the work proposed by Snare et al [7], the motion prior is local wherein, motion models at neighboring control points do not interact with each other. Thus if a control point falls in a poor contrast/signal dropout location, for a couple of frames, the segmentation will drift away.…”

Section: D Active Contour Formulation Using Non-local Temporal Priorsmentioning

confidence: 99%

Automated Intraventricular Septum Segmentation Using Non-local Spatio-temporal Priors

Gupta¹,

Thiruvenkadam²,

Subramanian³

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract. Automated robust segmentation of intra-ventricular septum (IVS) from B-mode echocardiographic images is an enabler for early quantification of cardiac disease. Segmentation of septum from ultrasound images is very challenging due to variations in intensity/contrast in and around the septum, speckle noise and non-rigid shape variations of the septum boundary. In this work, we effectively address these challenges using an approach that merges novel computer vision ideas with physiological markers present in cardiac scans. Specifically, we contribute towards the following: 1) A novel 1-D active contour segmentation approach that utilizes non-local (NL) temporal cues, 2) Robust initialization of the active contour framework, based on NL-means de-noising, and MRF based clustering that incorporates physiological cues. We validate our claims using cardiac measurement results on ∼30 cardiac scan videos (∼2000 ultrasound frames in total). Our method is fully automatic and near real time ( 0.1sec/frame) implementation.

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Automated septum thickness measurement—A Kalman filter approach

Cited by 6 publications

References 17 publications

Feasibility and clinical implementation of hand-held echocardiography

Feasibility and clinical implementation of hand-held echocardiography

Adaptive Kalman snake for semi-autonomous 3D vessel tracking

Automated Intraventricular Septum Segmentation Using Non-local Spatio-temporal Priors

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