Name
Abstract | ||
|---|---|---|
We have performed segmentation procedures on a large number of images from two mammalian cell lines that were seeded at low density, in order to study trends in the segmentation results and make predictions about cellular features that affect segmentation accuracy. By comparing segmentation results from approximately 40000 cells, we find a linear relationship between the highest segmentation accuracy seen for a given cell and the fraction of pixels in the neighborhood of the edge of that cell. This fraction of pixels is at greatest risk for error when cells are segmented. We call the ratio of the size of this pixel fraction to the size of the cell the extended edge neighborhood and this metric can predict segmentation accuracy of any isolated cell. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-17289-2_53 | ISVC (1) |
Keywords | Field | DocType |
segmentation accuracy,cellular feature,extended edge neighborhood,isolated cell,segmentation procedure,highest segmentation accuracy,biological cell image,predicting segmentation accuracy,segmentation result,greatest risk,pixel fraction,mammalian cell line,cell line | Canny edge detector,Computer vision,Scale-space segmentation,Pattern recognition,Range segmentation,Computer science,Segmentation,Image segmentation,Pixel,Region growing,Artificial intelligence,Minimum spanning tree-based segmentation | Conference |
Volume | ISSN | ISBN |
6453 | 0302-9743 | 3-642-17288-1 |
Citations | PageRank | References |
3 | 0.80 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adele P. Peskin | 1 | 33 | 6.79 |
| Alden A. Dima | 2 | 8 | 2.20 |
| Joe Chalfoun | 3 | 28 | 7.49 |
| John T. Elliott | 4 | 23 | 3.15 |