General Purpose Segmentation for Microorganisms in Microscopy Images

Publikation: Forskning - peer reviewKonferenceartikel i proceeding

Abstrakt

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.
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Detaljer

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.
OriginalsprogEngelsk
Titel9th International Conference on Computer Vision Theory and Applications
Antal sider6
UdgiverInstitute for Systems and Technologies of Information, Control and Communication
Publikationsdato2014
StatusUdgivet - 2014
Begivenhed - Lisbon, Danmark

Konference

KonferenceInternational Conference on Computer Vision Theory and Applications
LandDanmark
ByLisbon
Periode05/01/201408/01/2014

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