Details
| Original language | English |
|---|---|
| Title of host publication | 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '05) |
| Pages | II497-II500 |
| Volume | 5 |
| Publication status | Published - 2 May 2005 |
| Event | 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States Duration: 18 Mar 2005 → 23 Mar 2005 |
Publication series
| Name | IEEE International Conference on Acoustics, Speech and Signal Processing |
|---|---|
| ISSN (Print) | 1520-6149 |
Abstract
In this contribution an algorithm to estimate the cell density (cell count) from a still intensity image captured by an in-situ microscope directly from inside of a bioreactor is investigated. In comparison with other algorithms, ours has the advantage that it allows a reliable cell density estimation even though the cells build clusters in the scene. First, image regions containing at least one cell are segmented by applying a Maximum-Likelihood Thresholding technique. Then, the cell density inside of each segmented region is estimated by maximizing the variance of the circular Hough transform of the edges inside of it. The edges are extracted by applying the Smallest Univalue Segment Assimilating Nucleus Algorithm (SUSAN). The total cell density is the sum of the cell densities estimated inside of the segmented regions. The proposed algorithm has been implemented and applied to thousands of real images of cultures of mammalian Baby Hamster Kidney cells (BHK cells) captured by an in-situ microscope. The average of the percentage of the absolute cell density estimation error was 6.27%. The estimates are similar to those obtained with current off-the-shelf cell density monitoring instruments for cultures up to cell densities of 5x106 cells/mL
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Electrical and Electronic Engineering
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2005 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '05). Vol. 5 2005. p. II497-II500 (IEEE International Conference on Acoustics, Speech and Signal Processing).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Cell density estimation from a still image for in-situ microscopy
AU - Martinez, G.
AU - Frerichs, J. G.
AU - Joeris, K.
AU - Konstantinov, K.
AU - Scheper, T.
PY - 2005/5/2
Y1 - 2005/5/2
N2 - In this contribution an algorithm to estimate the cell density (cell count) from a still intensity image captured by an in-situ microscope directly from inside of a bioreactor is investigated. In comparison with other algorithms, ours has the advantage that it allows a reliable cell density estimation even though the cells build clusters in the scene. First, image regions containing at least one cell are segmented by applying a Maximum-Likelihood Thresholding technique. Then, the cell density inside of each segmented region is estimated by maximizing the variance of the circular Hough transform of the edges inside of it. The edges are extracted by applying the Smallest Univalue Segment Assimilating Nucleus Algorithm (SUSAN). The total cell density is the sum of the cell densities estimated inside of the segmented regions. The proposed algorithm has been implemented and applied to thousands of real images of cultures of mammalian Baby Hamster Kidney cells (BHK cells) captured by an in-situ microscope. The average of the percentage of the absolute cell density estimation error was 6.27%. The estimates are similar to those obtained with current off-the-shelf cell density monitoring instruments for cultures up to cell densities of 5x106 cells/mL
AB - In this contribution an algorithm to estimate the cell density (cell count) from a still intensity image captured by an in-situ microscope directly from inside of a bioreactor is investigated. In comparison with other algorithms, ours has the advantage that it allows a reliable cell density estimation even though the cells build clusters in the scene. First, image regions containing at least one cell are segmented by applying a Maximum-Likelihood Thresholding technique. Then, the cell density inside of each segmented region is estimated by maximizing the variance of the circular Hough transform of the edges inside of it. The edges are extracted by applying the Smallest Univalue Segment Assimilating Nucleus Algorithm (SUSAN). The total cell density is the sum of the cell densities estimated inside of the segmented regions. The proposed algorithm has been implemented and applied to thousands of real images of cultures of mammalian Baby Hamster Kidney cells (BHK cells) captured by an in-situ microscope. The average of the percentage of the absolute cell density estimation error was 6.27%. The estimates are similar to those obtained with current off-the-shelf cell density monitoring instruments for cultures up to cell densities of 5x106 cells/mL
UR - http://www.scopus.com/inward/record.url?scp=33646756899&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2005.1415450
DO - 10.1109/ICASSP.2005.1415450
M3 - Conference contribution
AN - SCOPUS:33646756899
SN - 0780388747
SN - 9780780388741
VL - 5
T3 - IEEE International Conference on Acoustics, Speech and Signal Processing
SP - II497-II500
BT - 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '05)
T2 - 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
Y2 - 18 March 2005 through 23 March 2005
ER -