Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2008 5th International Conference on Electrical Engineering, Computing Science and Automatic Control |
| Seiten | 168-172 |
| Seitenumfang | 5 |
| Publikationsstatus | Veröffentlicht - 22 Dez. 2008 |
| Veranstaltung | 2008 5th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2008 - Mexico City, Mexiko Dauer: 12 Nov. 2008 → 14 Nov. 2008 |
Abstract
In this contribution a new algorithm is proposed for segmenting the image regions of the cell clusters present in a static image captured by an in-situ microscope inside of a bioreactor. A cell cluster is a group of one or more cells that are very close to each other, almost overlapping. The new algorithm combines a contour based segmentation approach with a region based segmentation approach. First, seeds are selected only in the background. To this end, image contours and the first and second moments of the pixels' intensity values in the background and in the cell clusters are evaluated. The moments are estimated from the histogram of the pixels' intensity values by applying a Maximum-Likelihood estimator. Following, the background region is extracted by region growing from the selected seeds. Finally, the segmented regions of the cell clusters are those image regions which do not belong to the previously extracted background region. Experimental results show an improvement of 33.33% in the reliability and an improvement of 55.1% in the accuracy of the cell cluster segmentation results.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Angewandte Informatik
- Energie (insg.)
- Energieanlagenbau und Kraftwerkstechnik
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
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2008 5th International Conference on Electrical Engineering, Computing Science and Automatic Control. 2008. S. 168-172.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Region and Contour Based Cell Cluster Segmentation Algorithm for In-Situ Microscopy
AU - Sheehy, A.
AU - Martinez, G.
AU - Frerichs, J. G.
AU - Scheper, T.
PY - 2008/12/22
Y1 - 2008/12/22
N2 - In this contribution a new algorithm is proposed for segmenting the image regions of the cell clusters present in a static image captured by an in-situ microscope inside of a bioreactor. A cell cluster is a group of one or more cells that are very close to each other, almost overlapping. The new algorithm combines a contour based segmentation approach with a region based segmentation approach. First, seeds are selected only in the background. To this end, image contours and the first and second moments of the pixels' intensity values in the background and in the cell clusters are evaluated. The moments are estimated from the histogram of the pixels' intensity values by applying a Maximum-Likelihood estimator. Following, the background region is extracted by region growing from the selected seeds. Finally, the segmented regions of the cell clusters are those image regions which do not belong to the previously extracted background region. Experimental results show an improvement of 33.33% in the reliability and an improvement of 55.1% in the accuracy of the cell cluster segmentation results.
AB - In this contribution a new algorithm is proposed for segmenting the image regions of the cell clusters present in a static image captured by an in-situ microscope inside of a bioreactor. A cell cluster is a group of one or more cells that are very close to each other, almost overlapping. The new algorithm combines a contour based segmentation approach with a region based segmentation approach. First, seeds are selected only in the background. To this end, image contours and the first and second moments of the pixels' intensity values in the background and in the cell clusters are evaluated. The moments are estimated from the histogram of the pixels' intensity values by applying a Maximum-Likelihood estimator. Following, the background region is extracted by region growing from the selected seeds. Finally, the segmented regions of the cell clusters are those image regions which do not belong to the previously extracted background region. Experimental results show an improvement of 33.33% in the reliability and an improvement of 55.1% in the accuracy of the cell cluster segmentation results.
KW - Biomedical engineering
KW - Biomedical image processing
KW - Biomedical microscopy
KW - Biomedical monitoring
KW - Biomedical optical imaging
KW - Cell cluster segmentation
KW - Image segmentation
KW - In-situ microscopy
UR - http://www.scopus.com/inward/record.url?scp=61549096201&partnerID=8YFLogxK
U2 - 10.1109/ICEEE.2008.4723393
DO - 10.1109/ICEEE.2008.4723393
M3 - Conference contribution
AN - SCOPUS:61549096201
SN - 9781424424993
SP - 168
EP - 172
BT - 2008 5th International Conference on Electrical Engineering, Computing Science and Automatic Control
T2 - 2008 5th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2008
Y2 - 12 November 2008 through 14 November 2008
ER -