Details
| Original language | English |
|---|---|
| Title of host publication | 2014 International Conference on Electronics, Communications and Computers (CONIELECOMP) |
| Pages | 105-110 |
| Number of pages | 6 |
| ISBN (electronic) | 978-1-4799-3469-0, 978-1-4799-3468-3 |
| Publication status | Published - 1 May 2014 |
| Event | 24th International Conference on Electronics, Communications and Computers, CONIELECOMP 2014 - Cholula, Mexico Duration: 26 Feb 2014 → 28 Feb 2014 |
Abstract
An Algorithm is presented for accurate segmentation of single isolated human insulin crystals in an image captured by an in situ microscope inside of a bioreactor. It consists of three major steps. First, the foreground regions are extracted by thresholding the image. Then, the regions which correspond to the single isolated human insulin crystals are detected among the previously segmented foreground regions using a single nearest prototype rule, where each segmented foreground region class prototype represents a 7-dimensional mean vector of rotation, translation and scale invariant shape characteristics of several class members, which were extracted a priori from a training set of images. Finally, the contour accuracy of the detected regions is improved by moving each contour point to that image position where the weighted sum of the image intensity and the first and the second contour derivatives is minimal. The search of the minimum is carried out only along the line segment that goes from the region contour point position to the position of the region center of gravity. Experiments with 60 real images revealed very accurate segmentation results with an average contour accuracy of 1.61±2.53 pixel.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Engineering(all)
- Electrical and Electronic Engineering
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2014 International Conference on Electronics, Communications and Computers (CONIELECOMP). 2014. p. 105-110.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Accurate Segmentation of Single Isolated Human Insulin Crystals for In-Situ Microscopy
AU - Martinez, G.
AU - Lindner, P.
AU - Bluma, A.
AU - Scheper, T.
PY - 2014/5/1
Y1 - 2014/5/1
N2 - An Algorithm is presented for accurate segmentation of single isolated human insulin crystals in an image captured by an in situ microscope inside of a bioreactor. It consists of three major steps. First, the foreground regions are extracted by thresholding the image. Then, the regions which correspond to the single isolated human insulin crystals are detected among the previously segmented foreground regions using a single nearest prototype rule, where each segmented foreground region class prototype represents a 7-dimensional mean vector of rotation, translation and scale invariant shape characteristics of several class members, which were extracted a priori from a training set of images. Finally, the contour accuracy of the detected regions is improved by moving each contour point to that image position where the weighted sum of the image intensity and the first and the second contour derivatives is minimal. The search of the minimum is carried out only along the line segment that goes from the region contour point position to the position of the region center of gravity. Experiments with 60 real images revealed very accurate segmentation results with an average contour accuracy of 1.61±2.53 pixel.
AB - An Algorithm is presented for accurate segmentation of single isolated human insulin crystals in an image captured by an in situ microscope inside of a bioreactor. It consists of three major steps. First, the foreground regions are extracted by thresholding the image. Then, the regions which correspond to the single isolated human insulin crystals are detected among the previously segmented foreground regions using a single nearest prototype rule, where each segmented foreground region class prototype represents a 7-dimensional mean vector of rotation, translation and scale invariant shape characteristics of several class members, which were extracted a priori from a training set of images. Finally, the contour accuracy of the detected regions is improved by moving each contour point to that image position where the weighted sum of the image intensity and the first and the second contour derivatives is minimal. The search of the minimum is carried out only along the line segment that goes from the region contour point position to the position of the region center of gravity. Experiments with 60 real images revealed very accurate segmentation results with an average contour accuracy of 1.61±2.53 pixel.
UR - http://www.scopus.com/inward/record.url?scp=84901260712&partnerID=8YFLogxK
U2 - 10.1109/conielecomp.2014.6808576
DO - 10.1109/conielecomp.2014.6808576
M3 - Conference contribution
AN - SCOPUS:84901260712
SP - 105
EP - 110
BT - 2014 International Conference on Electronics, Communications and Computers (CONIELECOMP)
T2 - 24th International Conference on Electronics, Communications and Computers, CONIELECOMP 2014
Y2 - 26 February 2014 through 28 February 2014
ER -