Name
Abstract | ||
|---|---|---|
While several mosaicking algorithms have been developed to compose endoscopic images of the internal urinary bladder wall into panoramic images, the quantitative evaluation of these output images in terms of geometrical distortions have often not been discussed. However, the visualization of the distortion level is highly desired for an objective image-based medical diagnosis. Thus, we present in this paper a method to create quality maps from the characteristics of transformation parameters, which were applied to the endoscopic images during the registration process of the mosaicking algorithm. For a global first view impression, the quality maps are laid over the panoramic image and highlight image regions in pseudo-colors according to their local distortions. This illustration supports then surgeons to identify geometrically distorted structures easily in the panoramic image, which allow more objective medical interpretations of tumor tissue in shape and size. Aside from introducing quality maps in 2-D, we also discuss a visualization method to map panoramic images onto a 3-D spherical bladder model. Reference points are manually selected by the surgeon in the panoramic image and the 3-D model. Then the panoramic image is mapped by the Hammer-Ait-off equal-area projection onto the 3-D surface using texture mapping. Finally the textured bladder model can be freely moved in a virtual environment for inspection. Using a two-hemisphere bladder representation, references between panoramic image regions and their corresponding space coordinates within the bladder model are reconstructed. This additional spatial 3-D information thus assists the surgeon in navigation, documentation, as well as surgical planning. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1117/12.877630 | Proceedings of SPIE |
Keywords | Field | DocType |
Bladder,Fluorescence Endoscopy,Image Mosaicking,Quality Maps,Visualization | Computer vision,Texture mapping,Volume rendering,Surgical planning,Virtual reality,Computer graphics (images),Visualization,Impression,Artificial intelligence,Distortion,Medical diagnosis,Physics | Conference |
Volume | ISSN | Citations |
7964 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander Behrens | 1 | 80 | 11.81 |
| Iris Heisterklaus | 2 | 1 | 1.79 |
| yannick muller | 3 | 0 | 0.34 |
| Thomas Stehle | 4 | 80 | 11.55 |
| Sebastian Gross | 5 | 131 | 14.59 |
| Til Aach | 6 | 855 | 117.45 |