From Lines to Shapes: Geometric-Constrained Segmentation of X-Ray Collimators via Hough Transform
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909770764517376 |
|---|---|
| author | El-Zein, Benjamin Eckert, Dominik Fieselmann, Andreas Syben, Christopher Ritschl, Ludwig Kappler, Steffen Stober, Sebastian |
| author_facet | El-Zein, Benjamin Eckert, Dominik Fieselmann, Andreas Syben, Christopher Ritschl, Ludwig Kappler, Steffen Stober, Sebastian |
| contents | Collimation in X-ray imaging restricts exposure to the region-of-interest (ROI) and minimizes the radiation dose applied to the patient. The detection of collimator shadows is an essential image-based preprocessing step in digital radiography posing a challenge when edges get obscured by scattered X-ray radiation. Regardless, the prior knowledge that collimation forms polygonal-shaped shadows is evident. For this reason, we introduce a deep learning-based segmentation that is inherently constrained to its geometry. We achieve this by incorporating a differentiable Hough transform-based network to detect the collimation borders and enhance its capability to extract the information about the ROI center. During inference, we combine the information of both tasks to enable the generation of refined, line-constrained segmentation masks. We demonstrate robust reconstruction of collimated regions achieving median Hausdorff distances of 4.3-5.0mm on diverse test sets of real Xray images. While this application involves at most four shadow borders, our method is not fundamentally limited by a specific number of edges. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04437 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Lines to Shapes: Geometric-Constrained Segmentation of X-Ray Collimators via Hough Transform El-Zein, Benjamin Eckert, Dominik Fieselmann, Andreas Syben, Christopher Ritschl, Ludwig Kappler, Steffen Stober, Sebastian Computer Vision and Pattern Recognition Medical Physics Collimation in X-ray imaging restricts exposure to the region-of-interest (ROI) and minimizes the radiation dose applied to the patient. The detection of collimator shadows is an essential image-based preprocessing step in digital radiography posing a challenge when edges get obscured by scattered X-ray radiation. Regardless, the prior knowledge that collimation forms polygonal-shaped shadows is evident. For this reason, we introduce a deep learning-based segmentation that is inherently constrained to its geometry. We achieve this by incorporating a differentiable Hough transform-based network to detect the collimation borders and enhance its capability to extract the information about the ROI center. During inference, we combine the information of both tasks to enable the generation of refined, line-constrained segmentation masks. We demonstrate robust reconstruction of collimated regions achieving median Hausdorff distances of 4.3-5.0mm on diverse test sets of real Xray images. While this application involves at most four shadow borders, our method is not fundamentally limited by a specific number of edges. |
| title | From Lines to Shapes: Geometric-Constrained Segmentation of X-Ray Collimators via Hough Transform |
| topic | Computer Vision and Pattern Recognition Medical Physics |
| url | https://arxiv.org/abs/2509.04437 |