A deep learning approach for detecting and correcting highlights in endoscopic images

Antonio Rodriguez-Sanchez, Daly Chea, George Azzopardi, Sebastian Stabinger

OnderzoeksoutputAcademicpeer review

11 Citaten (Scopus)

Samenvatting

The image of an object changes dramatically depending on the lightning conditions surrounding that object. Shadows, reflections and highlights can make the object very difficult to be recognized for an automatic system. Additionally, images used in medical applications, such as endoscopic images and videos contain a large amount of such reflective components. This can pose an extra difficulty for experts to analyze such type of videos and images. It can then be useful to detect - and possibly correct - the locations where those highlights happen. In this work we designed a Convolutional Neural Network for that task. We trained such a network using a dataset that contains groundtruth highlights showing that those reflective elements can be learnt and thus located and extracted. We then used that trained network to localize and correct the highlights in endoscopic images from the El Salvador Atlas Gastrointestinal videos obtaining promising results.
Originele taal-2English
TitelSeventh International Conference on Image Processing Theory, Tools and Applications (IPTA)
UitgeverijIEEE
ISBN van elektronische versie 978-1-5386-1842-4
ISBN van geprinte versie978-1-5386-1843-1
DOI's
StatusPublished - 2017
EvenementSeventh International Conference on Image Processing Theory, Tools and Applications (IPTA) - Montreal, Canada
Duur: 28-nov.-20171-dec.-2017
http://www.ipta-conference.com/ipta17/

Conference

ConferenceSeventh International Conference on Image Processing Theory, Tools and Applications (IPTA)
Verkorte titelIPTA 2017
Land/RegioCanada
StadMontreal
Periode28/11/201701/12/2017
Internet adres

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