Name
Abstract | ||
|---|---|---|
This paper details the development of a novel method for automatic segmentation of collapsed colon lumen based on a prior knowl- edge of colon geometrical features and anatomical structure. After the re- moval of surrounding air voxels and lungs from the volumetric Computed Tomography(CT) data, labelling was performed to detect the remaining air voxel regions inside the CT data. Volume by length analysis, orien- tation, length, end points, geometrical position in the volumetric data, and gradient of centreline of each labelled air region were used as geo- metrical features to automatically segment the colon in CT data. The proposed method was validated using a total of 115 datasets. Collapsed colon surface detection was always higher than 95% with an average of 1.07% extra colonic surface inclusion. When the devised segmentation technique was applied to well-distended colon surface the colon detection was close to 100%. |
Year | Venue | Keywords |
|---|---|---|
2005 | IICAI | electronic engineering,image processing,ct |
Field | DocType | Citations |
Voxel,Computer vision,Colon.lumen,Pattern recognition,Segmentation,Computer science,Artificial intelligence,Volumetric Computed Tomography,Volumetric data | Conference | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tarik A. Chowdhury | 1 | 17 | 3.12 |
| Paul F. Whelan | 2 | 561 | 39.95 |
| Ovidiu Ghita | 3 | 234 | 18.12 |