Fast feature based multi slice to volume registration using phase congruency
Bibliographic record
Abstract
Slice to volume registration is very useful in many medical imaging applications, for example, fusing static high resolution three dimensional (3D) image volumes to dynamic two dimensional (2D) slice data for deriving motion information in 3D. Though information theoretic registration methods such as Mutual Information are usually robust, they are time intensive and typically require a high level of field-of-view correspondence between the source and target images. In single slice to volume registration scenarios, where such correspondence is limited, registration accuracy and robustness often deteriorate. In this paper, we present a novel registration method that maintains robustness and accuracy while significantly increasing registration speed. Our approach employs multiple slice (as opposed to single slice) to volume registration, which increases the amount of potential matching information while maintaining a small number of slices and hence facilitates the often necessary high speed dynamic image acquisition. Our proposed registration approach first extracts phase congruency information from the slices/volume using oriented 2D Gabor wavelets. Using local non maximum suppression, we then automatically obtain a robust and accurate set of feature points that are subsequently matched using an Iterative Closest Point (ICP) approach. Validation on BrainWeb simulated magnetic resonance imaging (MRI) data showed significant gains in speed ( approximately 40-fold increase) when compared to conventional Mutual Information based volumetric registration while maintaining comparable robustness and accuracy levels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".