Details
| Original language | English |
|---|---|
| Title of host publication | CONIELECOMP 2011, 21st International Conference on Electrical Communications and Computers |
| Pages | 5-9 |
| Number of pages | 5 |
| Publication status | Published - 11 Apr 2011 |
| Event | 21st International Conference on Electronics Communications and Computers, CONIELECOMP 2011 - Cholula, Mexico Duration: 28 Feb 2011 → 2 Mar 2011 |
Abstract
In this paper, an algorithm is introduced for segmenting the foreground regions present in a human insulin crystal intensity image captured by an in-situ microscope inside of a bioreactor. The segmentation is carried out by classifying all image pixels into pixels belonging to the foreground regions and pixels belonging to the background region. For classification, the local intensity variance at each pixel position is compared to a threshold. Those pixels whose local intensity variance is bigger than the threshold are classified as belonging to the foreground regions. The threshold is estimated as a linear combination of two statistical characteristics of the local intensity variance values at the pixels in the background region. Those statistical characteristics are estimated from the histogram of the local intensity variance values of all image pixels by maximizing a likelihood function using an Expectation and Maximization approach. Misclassifications are corrected by particle filtering. Experimental results on real data revealed a processing time of 11.82 seconds/image, an excellent reliability and a segmentation error of approximately 14 pixels.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Hardware and Architecture
- Engineering(all)
- Electrical and Electronic Engineering
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CONIELECOMP 2011, 21st International Conference on Electrical Communications and Computers. 2011. p. 5-9.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Foreground Segmentation of Human Insulin Crystal Images for In-Situ Microscopy
AU - Martinez, Geovanni
AU - Lindner, Patrick
AU - Scheper, Thomas
PY - 2011/4/11
Y1 - 2011/4/11
N2 - In this paper, an algorithm is introduced for segmenting the foreground regions present in a human insulin crystal intensity image captured by an in-situ microscope inside of a bioreactor. The segmentation is carried out by classifying all image pixels into pixels belonging to the foreground regions and pixels belonging to the background region. For classification, the local intensity variance at each pixel position is compared to a threshold. Those pixels whose local intensity variance is bigger than the threshold are classified as belonging to the foreground regions. The threshold is estimated as a linear combination of two statistical characteristics of the local intensity variance values at the pixels in the background region. Those statistical characteristics are estimated from the histogram of the local intensity variance values of all image pixels by maximizing a likelihood function using an Expectation and Maximization approach. Misclassifications are corrected by particle filtering. Experimental results on real data revealed a processing time of 11.82 seconds/image, an excellent reliability and a segmentation error of approximately 14 pixels.
AB - In this paper, an algorithm is introduced for segmenting the foreground regions present in a human insulin crystal intensity image captured by an in-situ microscope inside of a bioreactor. The segmentation is carried out by classifying all image pixels into pixels belonging to the foreground regions and pixels belonging to the background region. For classification, the local intensity variance at each pixel position is compared to a threshold. Those pixels whose local intensity variance is bigger than the threshold are classified as belonging to the foreground regions. The threshold is estimated as a linear combination of two statistical characteristics of the local intensity variance values at the pixels in the background region. Those statistical characteristics are estimated from the histogram of the local intensity variance values of all image pixels by maximizing a likelihood function using an Expectation and Maximization approach. Misclassifications are corrected by particle filtering. Experimental results on real data revealed a processing time of 11.82 seconds/image, an excellent reliability and a segmentation error of approximately 14 pixels.
UR - http://www.scopus.com/inward/record.url?scp=79955710336&partnerID=8YFLogxK
U2 - 10.1109/CONIELECOMP.2011.5749322
DO - 10.1109/CONIELECOMP.2011.5749322
M3 - Conference contribution
AN - SCOPUS:79955710336
SN - 9781424495573
SP - 5
EP - 9
BT - CONIELECOMP 2011, 21st International Conference on Electrical Communications and Computers
T2 - 21st International Conference on Electronics Communications and Computers, CONIELECOMP 2011
Y2 - 28 February 2011 through 2 March 2011
ER -