A novel outlier detection based approach to registering pre- and post-resection ultrasound brain tumor images

Chel, Haradhan; Bora, Prabin Kumar

doi:10.1109/icaee.2017.8255403

Cited by 3 publications

(1 citation statement)

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“…In addition, the high local temperatures during ablation therapy can create microbubbles, which can create nonlinear outlier samples. Moreover, outliers can be observed due to attenuation of ultrasound beam [61] while travelling through an attenuating media such as rib bone or skull. Similar phenomenon is observed in synthetic aperture imaging where acoustic energy is low.…”

Section: Introductionmentioning

confidence: 99%

Incorporating Gradient Similarity for Robust Time Delay Estimation in Ultrasound Elastography

Ashikuzzaman¹,

Hall²,

Rivaz³

2022

Preprint

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Energy-based ultrasound elastography techniques minimize a regularized cost function consisting of data and continuity terms to obtain local displacement estimates based on the local time-delay estimation (TDE) between radio-frequency (RF) frames. The data term associated with the existing techniques takes only the amplitude similarity into account and hence is not sufficiently robust to the outlier samples present in the RF frames under consideration. This drawback creates noticeable artifacts in the strain image. To resolve this issue, we propose to formulate the data function as a linear combination of the amplitude and gradient similarity constraints. We estimate the adaptive weight concerning each similarity term following an iterative scheme. Finally, we optimize the non-linear cost function in an efficient manner to convert the problem to a sparse system of linear equations which are solved for millions of variables. We call our technique rGLUE: robust data term in GLobal Ultrasound Elastography. rGLUE has been validated using simulation, phantom, in vivo liver, and breast datasets. In all of our experiments, rGLUE substantially outperforms the recent elastography methods both visually and quantitatively. For simulated, phantom, and in vivo datasets, respectively, rGLUE achieves 107%, 18%, and 23% improvements of signalto-noise ratio (SNR) and 61%, 19%, and 25% improvements of contrast-to-noise ratio (CNR) over GLUE, a recently-published elastography algorithm.

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Section: Introductionmentioning

confidence: 99%