Mosaicing of Bladder Endoscopic Image Sequences: Distortion Calibration and Registration Algorithm
Bibliographic record
Abstract
Cancers located on the internal wall of bladders can be detected in image sequences acquired with endoscopes. The clinical diagnosis and follow-up can be facilitated by building a unique panoramic image of the bladder with the images acquired from different viewpoints. This process, called image mosaicing, consists of two steps. In the first step, consecutive images are pairwise registered to find the local transformation matrices linking geometrically consecutive images. In the second step, all images are placed in a common and global coordinate system. In this contribution, a mutual information-based similarity measure and a stochastic gradient optimization method were implemented in the registration process. However, the images have to be preprocessed in order to register the data in a robust way. Thus, a simple correction method of the distortions affecting endoscopic images is presented. After the placement of all images in the global coordinate system, the parameters of the local transformation matrices are all adjusted to improve the visual aspect of the panoramic images. Phantoms are used to evaluate the global mosaicing accuracy and the limits of the registration algorithm. The mean distances between ground truth positions in the mosaiced image range typically in 1-3 pixels. Results given for in vivo patient data illustrate the ability of the algorithm to give coherent panoramic images in the case of bladders.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".