Markerless Inside-Out Tracking for 3D Ultrasound Compounding

Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation - Lecture Notes in Computer Science ◽

10.1007/978-3-030-01045-4_7 ◽

2018 ◽

pp. 56-64

Author(s):

Benjamin Busam ◽

Patrick Ruhkamp ◽

Salvatore Virga ◽

Beatrice Lentes ◽

Julia Rackerseder ◽

...

Keyword(s):

3D Ultrasound ◽

Ultrasound Compounding ◽

Inside Out

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Rapid image registration for 3D ultrasound compounding

2000 IEEE Ultrasonics Symposium. Proceedings. An International Symposium (Cat. No.00CH37121) ◽

10.1109/ultsym.2000.921626 ◽

2002 ◽

Cited By ~ 1

Author(s):

J.F. Kucker ◽

P.L. Carson ◽

G.L. LeCarpentier ◽

J.B. Fowlkes ◽

C.R. Meyer

Keyword(s):

Image Registration ◽

3D Ultrasound ◽

Ultrasound Compounding

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Good and bad boundaries in ultrasound compounding: preserving anatomic boundaries while suppressing artifacts

International Journal of Computer Assisted Radiology and Surgery ◽

10.1007/s11548-021-02464-4 ◽

2021 ◽

Author(s):

Alex Ling Yu Hung ◽

John Galeotti

Keyword(s):

Best Practices ◽

3D Ultrasound ◽

The Other ◽

Ultrasound Images ◽

Ultrasound Probe ◽

Single Image ◽

Volume Reconstruction ◽

View Reconstruction ◽

Ultrasound Compounding ◽

Future Utility

Abstract Purpose Ultrasound compounding is to combine sonographic information captured from different angles and produce a single image. It is important for multi-view reconstruction, but as of yet there is no consensus on best practices for compounding. Current popular methods inevitably suppress or altogether leave out bright or dark regions that are useful and potentially introduce new artifacts. In this work, we establish a new algorithm to compound the overlapping pixels from different viewpoints in ultrasound. Methods Inspired by image fusion algorithms and ultrasound confidence, we uniquely leverage Laplacian and Gaussian pyramids to preserve the maximum boundary contrast without overemphasizing noise, speckles, and other artifacts in the compounded image, while taking the direction of the ultrasound probe into account. Besides, we designed an algorithm that detects the useful boundaries in ultrasound images to further improve the boundary contrast. Results We evaluate our algorithm by comparing it with previous algorithms both qualitatively and quantitatively, and we show that our approach not only preserves both light and dark details, but also somewhat suppresses noise and artifacts, rather than amplifying them. We also show that our algorithm can improve the performance of downstream tasks like segmentation. Conclusion Our proposed method that is based on confidence, contrast, and both Gaussian and Laplacian pyramids appears to be better at preserving contrast at anatomic boundaries while suppressing artifacts than any of the other approaches we tested. This algorithm may have future utility with downstream tasks such as 3D ultrasound volume reconstruction and segmentation.

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The Structure Among Measures of Personality, Social Attitudes, Values, and Social Norms

Journal of Individual Differences ◽

10.1027/1614-0001.28.4.240 ◽

2007 ◽

Vol 28 (4) ◽

pp. 240-251 ◽

Cited By ~ 28

Author(s):

Lazar Stankov

Keyword(s):

Factor Analysis ◽

Individual Differences ◽

Social Norms ◽

Social Interactions ◽

Correlation Matrix ◽

Social Attitudes ◽

Group Differences ◽

Factor Scores ◽

Ethnic Group Differences ◽

Inside Out

Abstract. This paper presents the results of a study that employed measures of personality, social attitudes, values, and social norms that have been the focus of recent research in individual differences. These measures were given to a sample of participants (N = 1,255) who were enrolled at 25 US colleges and universities. Factor analysis of the correlation matrix produced four factors. Three of these factors corresponded to the domains of Personality/Amoral Social Attitudes, Values, and Social Norms; one factor, Conservatism, cut across the domains. Cognitive ability showed negative correlation with conservatism and amoral social attitudes. The study also examined gender and ethnic group differences on factor scores. The overall interpretation of the findings is consistent with the inside-out view of human social interactions.

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