Name
Title | ||
|---|---|---|
Denoising fluorescence endoscopy: a motion compensated temporal recursive video filter with an optimal minimum mean square error parameterization |
Abstract | ||
|---|---|---|
Fluorescence endoscopy is an emerging technique for the detection of bladder cancer. A marker substance is brought into the patient's bladder which accumulates at cancer tissue. If a suitable narrow band light source is used for illumination, a red fluorescence of the marker substance is observable. Because of the low fluorescence photon count and because of the narrow band light source, only a small amount of light is detected by the camera's CCD sensor. This, in turn, leads to strong noise in the recorded video sequence. To overcome this problem, we apply a temporal recursive filter to the video sequence. The derivation of a filter function is presented, which leads to an optimal filter in the minimum mean square error sense. The algorithm is implemented as plug-in for the real-time capable clinical demonstrator platform RealTimeFrame and it is capable to process color videos with a resolution of 768×576 pixels at 50 frames per second. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ISBI.2009.5193047 | ISBI |
Keywords | Field | DocType |
cancer,endoscopes,fluorescence,image denoising,mean square error methods,medical image processing,motion compensation,recursive filters,tumours,bladder cancer,cancer tissue,color videos,denoising,error parameterization,fluorescence endoscopy,fluorescence photon count,motion compensation,optimal minimum mean square error,temporal recursive filter,tumors,video filter,video sequence,Noise filtering,bladder,endoscopy,fluorescence,optimal filter,photo dynamic diagnostics | Noise reduction,Filter (video),Computer vision,Computer science,Motion compensation,Filter (signal processing),Minimum mean square error,Frame rate,Artificial intelligence,Pixel,Recursive filter | Conference |
Citations | PageRank | References |
1 | 0.37 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Stehle | 1 | 80 | 11.55 |
| Jonas Wulff | 2 | 438 | 17.59 |
| Alexander Behrens | 3 | 80 | 11.81 |
| Sebastian Gross | 4 | 131 | 14.59 |
| Til Aach | 5 | 855 | 117.45 |