Abstract
Three-Dimensional (3D) radiologic images are widely used to assess the condition of thin tubular structures, such as the pulmonary airways, coronary arteries, and colon. Precise 3D central axes of these structures are needed, however, for accurate quantization. Commonly employed manual-axes identification techniques are time-consuming and error-prone. Recently proposed automated techniques do not adequately exploit the available gray-scale or anatomic structural information and they are also prone to errors. We propose a method for computing the precise central axes of branching structures contained in 3D images. The method is robust to data anisotropy and uses true gray-scale information. These axes can then be used for automated navigation and assessment in a virtual-endoscopic system. We present application of our method to a human lung-cancer case.
| Original language | English (US) |
|---|---|
| Pages | 136-139 |
| Number of pages | 4 |
| State | Published - 1997 |
| Event | Proceedings of the 1997 International Conference on Image Processing. Part 2 (of 3) - Santa Barbara, CA, USA Duration: Oct 26 1997 → Oct 29 1997 |
Other
| Other | Proceedings of the 1997 International Conference on Image Processing. Part 2 (of 3) |
|---|---|
| City | Santa Barbara, CA, USA |
| Period | 10/26/97 → 10/29/97 |
All Science Journal Classification (ASJC) codes
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
