Abstract
Original language | English |
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Title of host publication | 9th International Conference on Computer Vision Theory and Applications |
Number of pages | 6 |
Publisher | Institute for Systems and Technologies of Information, Control and Communication |
Publication date | 2014 |
Publication status | Published - 2014 |
Event | International Conference on Computer Vision Theory and Applications - Lisbon, Denmark Duration: 5 Jan 2014 → 8 Jan 2014 |
Conference
Conference | International Conference on Computer Vision Theory and Applications |
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Country | Denmark |
City | Lisbon |
Period | 05/01/2014 → 08/01/2014 |
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General Purpose Segmentation for Microorganisms in Microscopy Images. / Jensen, Sebastian H. Nesgaard; Moeslund, Thomas B.; Rankl, Christian.
9th International Conference on Computer Vision Theory and Applications . Institute for Systems and Technologies of Information, Control and Communication, 2014.Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review
TY - GEN
T1 - General Purpose Segmentation for Microorganisms in Microscopy Images
AU - Jensen, Sebastian H. Nesgaard
AU - Moeslund, Thomas B.
AU - Rankl, Christian
PY - 2014
Y1 - 2014
N2 - In this paper, we propose an approach for achieving generalized segmentation of microorganisms in mi- croscopy images. It employs a pixel-wise classification strategy based on local features. Multilayer percep- trons are utilized for classification of the local features and is trained for each specific segmentation problem using supervised learning. This approach was tested on five different segmentation problems in bright field, differential interference contrast, fluorescence and laser confocal scanning microscopy. In all instance good results were achieved with the segmentation quality scoring a Dice coefficient of 0.831 or higher.
AB - In this paper, we propose an approach for achieving generalized segmentation of microorganisms in mi- croscopy images. It employs a pixel-wise classification strategy based on local features. Multilayer percep- trons are utilized for classification of the local features and is trained for each specific segmentation problem using supervised learning. This approach was tested on five different segmentation problems in bright field, differential interference contrast, fluorescence and laser confocal scanning microscopy. In all instance good results were achieved with the segmentation quality scoring a Dice coefficient of 0.831 or higher.
M3 - Article in proceeding
BT - 9th International Conference on Computer Vision Theory and Applications
PB - Institute for Systems and Technologies of Information, Control and Communication
ER -