| Paper: | WA-L1.5 |
| Session: | Image Processing and Analysis for Oncology |
| Time: | Wednesday, September 19, 11:30 - 11:50 |
| Presentation: |
Special Session Lecture
|
| Title: |
GEOMETRIC FEATURES BASED FRAMEWORK FOR COLONIC POLYP DETECTION USING A NEW COLOR CODING SCHEME |
| Authors: |
Dongqing Chen; University of Louisville | | |
| | Aly Farag; University of Louisville | | |
| | M. Sabry Hassouna; Vital Images, Inc. | | |
| | Rebort Falk; Jewish Hospital | | |
| | Gerald Dryden; University of Louisville | | |
| Abstract: |
Curvature-based geometric features have been proven to be important for colonic polyp detection. In this paper, we present an automatic detection framework and color coding scheme to highlight the detected polyps. The key idea is to place the detected polyps at the same locations in a newly created polygonal dataset with the same topology and geometry properties as the triangulated mesh surface of real colon dataset, and assign different colors to the two separated datasets to highlight the polyps. Finally, we validate the proposed framework by computer simulated and real colon datasets. For fifteen synthetic polyps with different shapes and different sizes, the sensitivity is 100%, and false positive is 0. For four real colon datasets, the proposed algorithm has achieved the sensitivity of 75%. |
