LV volume quantification via spatiotemporal analysis of real-time 3-D echocardiography

Angelini, Elsa D.; Laine, Andrew F.; Takuma, Shin; Holmes, Jeffrey W.; Homma, Shunichi

doi:10.1109/42.929612

Cited by 110 publications

(72 citation statements)

References 63 publications

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“…Because of characteristic US artefacts, such as speckle and shadowing, intensity inhomogeneities, low contrast and ill-defined boundaries, simple image feature-based thresholding or edge-detection methods are ineffective. Successful segmentation algorithms reported for US images are based on morphologic operations (Czerwinski et al 1999;Gong et al 2004), neural networks (Binder et al 1999), wavelet analysis (Angelini et al 2001) and Markov random fields (Haas et al 2000;Xiao et al 2002;Brusseau et al 2004;Gong et al 2004). These incorporate preprocessing for speckle reduction (e.g., the "stick" method) (Czerwinski et al 1999), anisotropic diffusion (Perona and Malik 1990) and intensity corrections (Xiao et al 2002).…”

Section: Introductionmentioning

confidence: 99%

Segmentation of elastographic images using a coarse-to-fine active contour model

Liu¹,

Zagzebski²,

Varghese³

et al. 2006

Ultrasound in Medicine & Biology

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Delineation of radiofrequency-ablation-induced coagulation (thermal lesion) boundaries is an important clinical problem that is not well addressed by conventional imaging modalities. Elastography, which produces images of the local strain after small, externally applied compressions, can be used for visualization of thermal coagulations. This paper presents an automated segmentation approach for thermal coagulations on 3-D elastographic data to obtain both area and volume information rapidly. The approach consists of a coarse-to-fine method for active contour initialization and a gradient vector flow, active contour model for deformable contour optimization with the help of prior knowledge of the geometry of general thermal coagulations. The performance of the algorithm has been shown to be comparable to manual delineation of coagulations on elastograms by medical physicists (r = 0.99 for volumes of 36 radiofrequency-induced coagulations). Furthermore, the automatic algorithm applied to elastograms yielded results that agreed with manual delineation of coagulations on pathology images (r = 0.96 for the same 36 lesions). This algorithm has also been successfully applied on in vivo elastograms.

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Section: Introductionmentioning

confidence: 99%

Segmentation of elastographic images using a coarse-to-fine active contour model

Liu¹,

Zagzebski²,

Varghese³

et al. 2006

Ultrasound in Medicine & Biology

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“…Most ultrasound segmentation algorithms are evaluated by comparison with manual segmentation [4][5][6][7]. The algorithm is considered validated if its performance measures are within the variation range of manual segmentation results.…”

Section: Evaluating and Explaining Accuracymentioning

confidence: 99%

Evaluation of a Cardiac Ultrasound Segmentation Algorithm Using a Phantom

Yue

Tagare

Madsen

et al. 2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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This paper evaluates the performance of a level set algorithm for segmenting the endocardium in short-axis ultrasound images. The evaluation is carried out using an anthropomorphic ultrasound phantom. Details of the phantom design, including comparison of the ultrasound parameters with invitro measurements, are included.In addition to measuring segmentation accuracy, the effectiveness of the energy minimization scheme is also determined. It is argued that using the phantom along with global minimization algorithms (simulated annealing and random search) makes is possible to assess the minimization strategy.

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“…Model-based approaches are more efficient when dealing with echocardiographic images due to the high signal-to-noise ratio. One can also separate the techniques that perform filtering/segmentation in the polar [7] or in the raw space [1]. Markov random fields formulations [7], active shape and appearance models [4,9], snakes [3], deformable models and templates [8] and level set techniques [1] are well established techniques considered to address the segmentation of the left ventricle in echocardiographic images.…”

Section: Introductionmentioning

confidence: 99%

“…Segmentation techniques for ultrasonic images consist of model-free [7,1] and model-based approaches [3,9]. Model-free techniques can better capture the variations of the endocardium while suffering from not being able to deal with the corrupted data.…”

Section: Introductionmentioning

confidence: 99%

Border Detection on Short Axis Echocardiographic Views Using a Region Based Ellipse-Driven Framework

Taron

Paragios

Jolly

2004

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004

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Abstract. In this paper, we propose a robust technique that integrates spatial and temporal information for consistent recovery of the endocardium. To account for the low image quality we introduce a local variant of the Mumford-Shah that is coupled with a model of limited parameters to describe the ventricle, namely an ellipse. The objective function is defined on the implicit space of ellipses, separates locally the blood pool from the heart wall and explores geometric constraints on the deformations of the endocardium to impose temporal consistency. Promising experimental results demonstrate the potentials of our method.

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LV volume quantification via spatiotemporal analysis of real-time 3-D echocardiography

Cited by 110 publications

References 63 publications

Segmentation of elastographic images using a coarse-to-fine active contour model

Segmentation of elastographic images using a coarse-to-fine active contour model

Evaluation of a Cardiac Ultrasound Segmentation Algorithm Using a Phantom

Border Detection on Short Axis Echocardiographic Views Using a Region Based Ellipse-Driven Framework

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