Name
Abstract | ||
|---|---|---|
A global representation of polyp and non polyp shapes are constructed following a point distribution model (PDM) as an alternative to current methods which only inspect local shape characteristics at a point on the surface. The decision on whether or not a candidate lesion is a polyp can then be made by comparing the minimum Euclidean distance of the candidate lesion to the constructed mean shapes. The model closer in distance to the candidate lesion is selected to represent that particular lesion -polyp or non polyp. This shape model can also be used to investigate the shape variability of the different lesions detected by constructing an allowable shape domain for each of these lesions. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/DICTA.2008.9 | DICTA |
Keywords | Field | DocType |
shape model,non polyp shape,different lesion,non polyp,ct colonography,mean shape models,allowable shape domain,particular lesion,local shape characteristic,polyp detection,candidate lesion,mean shape,shape variability,point distribution model,shape,current transformers,cancer,euclidean distance,computed tomography | Point distribution model,Computer vision,Pattern recognition,Lesion,Computer science,Euclidean distance,Computed tomography,Artificial intelligence | Conference |
Citations | PageRank | References |
3 | 0.38 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ju Lynn Ong | 1 | 31 | 4.45 |
| Abd-Krim Seghouane | 2 | 193 | 24.99 |
| Kevin Osborn | 3 | 6 | 0.85 |