Artifact removal and texture-based rendering for visualization of 3D fetal ultrasound images

Wang, Shyh-Roei; Sun, Yung-Nien; Chang, Feng-Cheng

doi:10.1007/s11517-007-0286-7

Cited by 4 publications

(3 citation statements)

References 34 publications

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“…The image of topography in the ultrasound image is a bright band but it may be affected by flow erosion and suspended particles [14]. Possibly, there are some artifacts [15] on the boundary lines where the configuration of the ultrasound imaging device is not completely correct [16]. But most of the artifacts are not as strong or bright as the imaging band of topography.…”

Section: Imaging System and Analysismentioning

confidence: 99%

Ultrasound imaging measurement of submerged topography in the muddy water physical model

Xiao

Guo

et al. 2015

Meas. Sci. Technol.

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The real-time, accurate measurement of submerged topography is vital for the analysis of riverbed erosion and deposition. This paper describes a novel method of measuring submerged topography in the B-scan image obtained using an ultrasound imaging device. Results show the distribution of gray values in the image has a process of mutation. This mutation process can be used to adaptively track the topographic lines between riverbed and water, based on the continuity of topography in the horizontal direction. The extracted topographic lines, of one pixel width, are processed by a wavelet filtering method. Compared with the actual topography, the measurement accuracy is within 1 mm. It is suitable for the real-time measurement and analysis of all current model topographies with the advantage of good self-adaptation. In particular, it is visible and intuitive for muddy water in the movable-bed model experiment.

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Section: Imaging System and Analysismentioning

confidence: 99%

Ultrasound imaging measurement of submerged topography in the muddy water physical model

Xiao

Guo

et al. 2015

Meas. Sci. Technol.

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“…Oriented splatting creates triangles aligned with the gradient of the surface function, the triangle is then colored with a Gaussian function and rendered in a back-to-front order. Wang et al proposed an improved surface rendering technique for 3D ultrasound data of fetuses [77]. To remove the noise and to preserve edges, a modified anisotropic diffusion is first applied to the dataset.…”

Section: Renderingmentioning

confidence: 99%

The Ultrasound Visualization Pipeline

Birkeland

Šoltészová

Hönigmann

et al. 2014

Mathematics and Visualization

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Note: Also to appear in the Dagstuhl 2012 SciVis book by Springer. Please cite this paper with its arXiv citation information. Abstract Ultrasound is one of the most frequently used imaging modality in medicine. The high spatial resolution, its interactive nature and non-invasiveness makes it the first choice in many examinations. Image interpretation is one of ultrasound's main challenges. Much training is required to obtain a confident skill level in ultrasound-based diagnostics. State-of-the-art graphics techniques is needed to provide meaningful visualizations of ultrasound in real-time. In this paper we present the process-pipeline for ultrasound visualization, including an overview of the tasks performed in the specific steps. To provide an insight into the trends of ultrasound visualization research, we have selected a set of significant publications and divided them into a technique-based taxonomy covering the topics pre-processing, segmentation, registration, rendering and augmented reality. For the different technique types we discuss the difference between ultrasound-based techniques and techniques for other modalities.

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“…When errors between network outputs and expected outputs are small enough which means the relationship between inputs and outputs has been memorized, the learning process is ceased. Thus, The support vector machine (SVM) [20] also has been used in the fetal weight estimation [18]. The SVM is based on the statistic learning theory and Structural Risk Minimization (SRM) principle which pursues minimizing the upper bound on the expected risk.…”

Section: Fetal Weight Estimation Based On Ultrasound Measurementmentioning

confidence: 99%

Fetal ultrasound image segmentation system and its use in fetal weight estimation

Wang

Chen

2008

Med Biol Eng Comput

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A semi-automated fetal ultrasound image segmentation system is developed to improve the estimation of fetal weight (EFW). Four standardized fetal parameters are measured by the proposed segmentation system: biparietal diameter, head circumference, abdominal circumference and femur length. Computerized measurements of 215 fetuses are compared with manual measurements in term of fitness analysis and difference analysis. Among 215 cases, computerized measurements of 103 fetuses within 3 days of delivery are utilized in the fetal weight estimation. The EFW based on computerized measurements and manual measurements are compared by using regression analysis, artificial neural network and support vector regression. By using different estimation methods, the computerized measurements decrease the EFW errors about 40-70 g. The lowest mean absolute percentage error of EFW decrease from 6.71% for manual measurements to 4.66% for computerized measurements. The proposed fetal ultrasound image segmentation system can provide more accurate EFW in antepartum examination.

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Artifact removal and texture-based rendering for visualization of 3D fetal ultrasound images

Cited by 4 publications

References 34 publications

Ultrasound imaging measurement of submerged topography in the muddy water physical model

Ultrasound imaging measurement of submerged topography in the muddy water physical model

The Ultrasound Visualization Pipeline

Fetal ultrasound image segmentation system and its use in fetal weight estimation

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