Optical Flow Active Contours with Primitive Shape Priors for Echocardiography

Hamou, Ali; El-Sakka, Mahmoud R.

doi:10.1155/2010/836753

Cited by 17 publications

(13 citation statements)

References 22 publications

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“…This yields a solution that is more optimal than that of other methods that first calculate the displacement of the contour points using OF tracking before feeding it to an active snake algorithm as an initial contour (Mikić et al, 1998) or as an additional force term (Hamou and El-Sakka, 2010). This method required manual input of the contour on the first frame.…”

Section: Contributing Ratersmentioning

confidence: 99%

A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images

Suinesiaputra

Cowan

Al-Agamy

et al. 2014

Medical Image Analysis

168

121

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A collaborative framework was initiated to establish a community resource of ground truth segmentations from cardiac MRI. Multi-site, multi-vendor cardiac MRI datasets comprising 95 patients (73 men, 22 women; mean age 62.73 ± 11.24 years) with coronary artery disease and prior myocardial infarction, were randomly selected from data made available by the Cardiac Atlas Project (Fonseca et al., 2011). Three semi- and two fully-automated raters segmented the left ventricular myocardium from short-axis cardiac MR images as part of a challenge introduced at the STACOM 2011 MICCAI workshop (Suinesiaputra et al., 2012). Consensus myocardium images were generated based on the Expectation-Maximization principle implemented by the STAPLE algorithm (Warfield et al., 2004). The mean sensitivity, specificity, positive predictive and negative predictive values ranged between 0.63-0.85, 0.60-0.98, 0.56-0.94 and 0.83-0.92, respectively, against the STAPLE consensus. Spatial and temporal agreement varied in different amounts for each rater. STAPLE produced high quality consensus images if the region of interest was limited to the area of discrepancy between raters. To maintain the quality of the consensus, an objective measure based on the candidate automated rater performance distribution is proposed. The consensus segmentation based on a combination of manual and automated raters were more consistent than any particular rater, even those with manual input. The consensus is expected to improve with the addition of new automated contributions. This resource is open for future contributions, and is available as a test bed for the evaluation of new segmentation algorithms, through the Cardiac Atlas Project (www.cardiacatlas.org).

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Section: Contributing Ratersmentioning

confidence: 99%

A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images

Suinesiaputra

Cowan

Al-Agamy

et al. 2014

Medical Image Analysis

168

121

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“…For motion estimation of biological structures from a subcellular, cellular to a supracellular level, PDE approaches based on optical flow (OF) vector fields have become increasingly important [14][15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Performance of optical flow techniques for motion analysis of fluorescent point signals in confocal microscopy

Delpiano

Jara

Scheer

et al. 2011

Machine Vision and Applications

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Artículo de publicación ISIOptical flow approaches calculate vector fields which determine the apparent velocities of objects in timevarying image sequences. They have been analyzed extensively in computer science using both natural and synthetic video sequences. In life sciences, there is an increasing need to extract kinetic information from temporal image sequences which reveals the interplay between form and function of microscopic biological structures. In this work, we test different variational optical flow techniques to quantify the displacements of biological objects in 2D fluorescent image sequences. The accuracy of the vector fields is tested for defined displacements of fluorescent point sources in synthetic image series which mimic protein traffic in neuronal dendrites, and for GABABR1 receptor subunits in dendrites of hippocampal neurons. Our results reveal that optical flow fields predict the movement of fluorescent point sources within an error of 3% for a maximum displacement of 160 nm. Displacement of agglomerated GABABR1 receptor subunits can be predicted correctly for maximum displacements of 640 nm. Based on these results, we introduce a criteria to derive the optimum parameter combinations for the calculation of the optical flow fields in experimental images. From these results, temporal sampling frequencies for image acquisition can be derived to guarantee correct motion estimation for biological objects.J. Delpiano and J. Jara are funded by a PhD scholarship from CONICYT (Chile). O. A. Ramírez is funded by FONDECYT (3110157). Research in SCIAN-Lab (S. Härtel) is funded by FONDECYT (1090246) and FONDEF (D07I1019). SCIAN-Lab is member of the German-Chilean Center of Excellence Initiative for Medical Informatics (DAAD), BNI (ICM P09-015-F), and the Advanced Imaging & Bioinformatics Initiative AI·BI (http://www.aibi.cl)

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“…Three evaluation indicators, including correctness [37][38][39][40], completeness [37][38][39][40], and area overlap measure (AOM) [41][42][43][44][45], are adopted in this paper to quantitatively assess the accuracy of the algorithm-extracted boundaries. Among them, correctness is to assess the boundary positioning accuracy from the aspect of polyline feature; completeness focuses on evaluating the completeness of extracted boundaries as a linear feature; and AOM is the rough estimate of the accuracy as a whole from the perspective of the polygon feature by calculating the percentage of the area that is correctly extracted.…”

Section: Performance Measurementioning

confidence: 99%

“…It can be acquired by computing the lengths of true negative (TN) boundaries based on the reference boundaries, as described in Equation (5) [37][38][39][40]. TN boundaries refer to the reference boundary segments lying within the buffer zones of the extracted boundaries, as shown in Figure 10.…”

Section: Completenessmentioning

confidence: 99%

Topology Adaptive Water Boundary Extraction Based on a Modified Balloon Snake: Using GF-1 Satellite Images as an Example

Chen

Liu

2017

Remote Sensing

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Topology adaptive water boundary extraction from satellite images using parametric snakes remains challenging in the domain of image segmentation. This paper proposed a modified balloon snake (MB-Snake) method based on the balloon snake (B-Snake) method, overcoming the B-Snake's drawbacks of inaccurate positioning, topology inflexibility, and non-automatic contour evolution termination. Six satellite images, acquired by the GF-1 wide field of view sensor and with water bodies of different types, inner land numbers, areas, boundary and background complexities, and digital number value contrasts, were used as experimental images, in which the MB-Snake method, and two comparison methods, the B-Snake and the orthogonal topology adaptive snake (OT-Snake) methods, were applied for water boundary extraction. All the extracted results were first qualitatively assessed and further quantitatively evaluated via three indexes, including correctness, completeness, and area overlap measure. Both of the qualitative and quantitative evaluation results consistently demonstrated that the MB-Snake method can efficiently improve the positioning accuracy, detect and dispose of topology collisions, and perform automatic contour evolution termination, successfully meeting its design objectives, and exhibiting great superiority to the existing topology-flexible parametric snakes. The sensitivity to initial contours, the effects of model parameters, and spatial resolutions of satellite images, and image demands of the MB-Snake method was also analyzed.

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Optical Flow Active Contours with Primitive Shape Priors for Echocardiography

Cited by 17 publications

References 22 publications

A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images

A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images

Performance of optical flow techniques for motion analysis of fluorescent point signals in confocal microscopy

Topology Adaptive Water Boundary Extraction Based on a Modified Balloon Snake: Using GF-1 Satellite Images as an Example

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