Abstract
Long-bone fractures such as femur fractures are very
common in trauma centers. Robotic assisted fracture surgery
(RAFS) can facilitate the minimally invasive surgery which
reduces scarring, infection risk and long hospital stays. One
important step in RAFS is to establish the coordinate system
link between patient joint (rigidly connected with the robotic
system) and an external tracking system. As X-ray
fluoroscopic images are routinely used during the procedure,
an automatic method is proposed to detect and localize
landmarks on the tracking tool using live X-ray image. The
proposed method uses combination of block detection,
geometric model matching and principle component
analysis. A successful rate of 91.3% is achieved after testing
on 650 X-ray images and accuracy is within 0.5 mm.
common in trauma centers. Robotic assisted fracture surgery
(RAFS) can facilitate the minimally invasive surgery which
reduces scarring, infection risk and long hospital stays. One
important step in RAFS is to establish the coordinate system
link between patient joint (rigidly connected with the robotic
system) and an external tracking system. As X-ray
fluoroscopic images are routinely used during the procedure,
an automatic method is proposed to detect and localize
landmarks on the tracking tool using live X-ray image. The
proposed method uses combination of block detection,
geometric model matching and principle component
analysis. A successful rate of 91.3% is achieved after testing
on 650 X-ray images and accuracy is within 0.5 mm.
| Original language | English |
|---|---|
| Publication status | Published - 2017 |
| Event | IEEE International Conference on Internet of Things - Exeter, United Kingdom Duration: 21 Jun 2017 → 23 Jun 2017 Conference number: 10 |
Conference
| Conference | IEEE International Conference on Internet of Things |
|---|---|
| Abbreviated title | iThings 2017 |
| Country/Territory | United Kingdom |
| City | Exeter |
| Period | 21/06/17 → 23/06/17 |