Ultrasound confidence maps using random walks

Karamalis, Athanasios; Wein, Wolfgang; Klein, Tassilo; Navab, Nassir

doi:10.1016/j.media.2012.07.005

Cited by 140 publications

(124 citation statements)

References 37 publications

(50 reference statements)

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“…The PS surface for both methods was based on empirical filter parameters set to values similar to those described in [6]: , , , and . Also, similar to what was described in [11], we used , , and , for generating the confidence map. Throughout our study, these selected parameters were not changed.…”

Section: Resultsmentioning

confidence: 99%

“…controls robustness of the overall result of random walk. However, barely affects the resulting confidence maps between the range to [11].…”

Section: Attenuation and Shadowing Featurementioning

confidence: 99%

“…Similar to Karamalis et al [11] we calculate a confidence map to quantify both the attenuation and shadowing properties in US images. We run a random walk [12] to calculate virtual signal strengths at every pixel in each ultrasound slice, given a signal being transmitted from virtual transducer locations at the top of the image slice.…”

Section: Attenuation and Shadowing Featurementioning

confidence: 99%

“…The solution for the desired unknown probabilities of unmarked nodes can be found using , where represents the known unit probabilities at the seed-points [11]. The edge-weights, , are then assigned in the horizontal, vertical and diagonal direction:…”

Section: Attenuation and Shadowing Featurementioning

confidence: 99%

“…The second key feature of bone material that we use is its significantly higher US attenuation effect compared to other tissues, which results in the characteristic shadowing below the bone surface in the US image. To quantify this shadowing and attenuation feature, we employ Karamalis et al's [11] shadow detection algorithm, which extracts a transmission model for an US image. Finally, we combine the aforementioned features into a hybrid feature that we call confidence-in-phase-symmetry (CPS) and that is intended to augment the PS measure in regions where shadowing and attenuation is large.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Confidence Weighted Local Phase Features for Robust Bone Surface Segmentation in Ultrasound

Quader

Hodgson

Abugharbieh

2014

Clinical Image-Based Procedures. Translational Research in Medical Imaging

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Abstract. Ultrasound (US) image guidance in orthopaedic surgery is emerging as a viable non-invasive alternative to the currently dominant radiation-based modalities. Though it offers many advantages including reduced imaging costs and safer operation, the relatively low US image quality complicates data processing and visualization. We propose a novel approach for robust bone localization that integrates multiple US image features including local phase information, local signal attenuation, and bone shadowing to robustly segment bone surfaces. We demonstrate the advantages of our approach in different contexts including improved segmentation quality, increased registration accuracy, and decreased sensitivity to parameter setting. We present quantitative and qualitative validation on a bovine femur phantom and on real-life clinical pelvis US data from 18 trauma patients using computed tomography (CT) image sets as ground truth.

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Section: Resultsmentioning

confidence: 99%

“…controls robustness of the overall result of random walk. However, barely affects the resulting confidence maps between the range to [11].…”

Section: Attenuation and Shadowing Featurementioning

confidence: 99%

Section: Attenuation and Shadowing Featurementioning

confidence: 99%

Section: Attenuation and Shadowing Featurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Confidence Weighted Local Phase Features for Robust Bone Surface Segmentation in Ultrasound

Quader

Hodgson

Abugharbieh

2014

Clinical Image-Based Procedures. Translational Research in Medical Imaging

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show abstract

Automatic Shadow Detection in 2D Ultrasound Images

Meng

Baumgartner

Sinclair

et al. 2018

Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis

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Automatically detecting acoustic shadows is of great importance for automatic 2D ultrasound analysis ranging from anatomy segmentation to landmark detection. However, variation in shape and similarity in intensity to other structures in the image make shadow detection a very challenging task. In this paper, we propose an automatic shadow detection method to generate a pixel-wise shadow confidence map from weakly labelled annotations. Our method jointly uses; (1) a feature attribution map from a Wasserstein GAN and (2) an intensity saliency map from a graph cut model. The proposed method accurately highlights the shadow areas in two 2D ultrasound datasets comprising standard view planes as acquired during fetal screening. Moreover, the proposed method outperforms the state-of-the-art quantitatively and improves failure cases for automatic biometric measurement.

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