Sci—Wed PM: Delivery—09: Automatic Contrast Enhancement on Electronic Portal Images Based on Human Visual System Properties
Bibliographic record
Abstract
Image Guided Radiation Therapy (IGRT) is one of the most efficient ways to treat prostate cancer. It is well‐known that Electronic Portal Images (EPI) acquired prior to the radiation therapy are of poor quality due to the low radiation dose. Several image parameters need to be manually adjusted in order to be able to visualize and localize the fiducial markers (FMs) seeds at the beginning of each day of treatment. This localization process is essential for the proper alignment of the patient. In this paper, we propose a novel technique for automatic image enhancement. There are many challenges involved in the automation of the image enhancement for FM seed detection. An automated enhancement algorithm is image and noise dependant. The level of noise in an input image and global image properties such as average contrast and intensity vary significantly between different patients and even for the same patient between different image acquisition sessions. Moreover, the small size of the targets (the FM seeds) makes it very difficult to distinguish them from noise, and hence to enhance them. Our approach addresses these challenges by using a contrast enhancement scheme based on properties of the human visual system (HVS). The rationale behind considering this approach lies in the ability of the HVS to adapt to detect objects of small size under low contrast and noise conditions. Our main contribution lies in the automatic set‐up of the parameters based upon a maxima search over a contrast metric.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".