Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro |
| Seiten | 542-545 |
| Seitenumfang | 4 |
| Publikationsstatus | Veröffentlicht - 8 Mai 2006 |
| Veranstaltung | 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Arlington, VA, USA / Vereinigte Staaten Dauer: 6 Apr. 2006 → 9 Apr. 2006 |
Publikationsreihe
| Name | International Symposium on Biomedical Imaging |
|---|
Abstract
This paper describes a new cell cluster segmentation algorithm based on global and local thresholding for in-situ microscopy. The global threshold is estimated by applying a known Maximum Likelihood Thresholding technique. Assuming that the background pixels around a cluster have similar intensity values, the local threshold used to improve the segmented region after global thresholding is estimated as the average of the intensity values of a set of selected surrounding background pixels of that region. First, all pixels on the border of the segmented region are defined as possible candidates of surrounding background pixels. Then, an algorithm based on RANSAC (RANdom SAmple Consensus) is applied to detect outliers within the candidates. Only the inliers are used for estimation of the local threshold value. The algorithm was applied to real intensity images captured by an in-situ microscope. The experimental results show that the segmentation accuracy improved by 8- %.
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2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 2006. S. 542-545 (International Symposium on Biomedical Imaging).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Cell cluster segmentation based on global and local thresholding for in-situ microscopy
AU - Espinoza, E.
AU - Martinez, G.
AU - Frerichs, J. G.
AU - Scheper, T.
PY - 2006/5/8
Y1 - 2006/5/8
N2 - This paper describes a new cell cluster segmentation algorithm based on global and local thresholding for in-situ microscopy. The global threshold is estimated by applying a known Maximum Likelihood Thresholding technique. Assuming that the background pixels around a cluster have similar intensity values, the local threshold used to improve the segmented region after global thresholding is estimated as the average of the intensity values of a set of selected surrounding background pixels of that region. First, all pixels on the border of the segmented region are defined as possible candidates of surrounding background pixels. Then, an algorithm based on RANSAC (RANdom SAmple Consensus) is applied to detect outliers within the candidates. Only the inliers are used for estimation of the local threshold value. The algorithm was applied to real intensity images captured by an in-situ microscope. The experimental results show that the segmentation accuracy improved by 8- %.
AB - This paper describes a new cell cluster segmentation algorithm based on global and local thresholding for in-situ microscopy. The global threshold is estimated by applying a known Maximum Likelihood Thresholding technique. Assuming that the background pixels around a cluster have similar intensity values, the local threshold used to improve the segmented region after global thresholding is estimated as the average of the intensity values of a set of selected surrounding background pixels of that region. First, all pixels on the border of the segmented region are defined as possible candidates of surrounding background pixels. Then, an algorithm based on RANSAC (RANdom SAmple Consensus) is applied to detect outliers within the candidates. Only the inliers are used for estimation of the local threshold value. The algorithm was applied to real intensity images captured by an in-situ microscope. The experimental results show that the segmentation accuracy improved by 8- %.
UR - http://www.scopus.com/inward/record.url?scp=33750936041&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2006.1624973
DO - 10.1109/ISBI.2006.1624973
M3 - Conference contribution
AN - SCOPUS:33750936041
SN - 0780395778
SN - 9780780395770
T3 - International Symposium on Biomedical Imaging
SP - 542
EP - 545
BT - 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro
T2 - 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro
Y2 - 6 April 2006 through 9 April 2006
ER -