Vis enkel innførsel

dc.contributor.authorWang, Congcong
dc.contributor.authorSharma, Vivek
dc.contributor.authorFan, Yu
dc.contributor.authorAlaya Cheikh, Faouzi
dc.contributor.authorBeghdadi, Azeddine
dc.contributor.authorElle, Ole Jacob
dc.contributor.authorStiefelhagen, Rainer
dc.date.accessioned2019-07-04T12:23:18Z
dc.date.available2019-07-04T12:23:18Z
dc.date.created2019-01-19T14:16:39Z
dc.date.issued2018
dc.identifier.citationFinal program and proceedings CIC26: Twenty-sixth Color and Imaging Conference 2018., 163-168.nb_NO
dc.identifier.issn2166-9635
dc.identifier.urihttp://hdl.handle.net/11250/2603421
dc.description.abstractLaparoscopic surgery has a limited field of view. Laser ablation in a laproscopic surgery causes smoke, which inevitably influences the surgeon's visibility. Therefore, it is of vital importance to remove the smoke, such that a clear visualization is possible. In order to employ a desmoking technique, one needs to know beforehand if the image contains smoke or not, to this date, there exists no accurate method that could classify the smoke/non-smoke images completely. In this work, we propose a new enhancement method which enhances the informative details in the RGB images for discrimination of smoke/non-smoke images. Our proposed method utilizes weighted least squares optimization framework (WLS). For feature extraction, we use statistical features based on bivariate histogram distribution of gradient magnitude (GM) and Laplacian of Gaussian (LoG). We then train a SVM classifier with binary smoke/non-smoke classification task. We demonstrate the effectiveness of our method on Cholec80 dataset. Experiments using our proposed enhancement method show promising results with improvements of 4% in accuracy and 4% in FI-Score over the baseline performance of RGB images. In addition, our approach improves over the saturation histogram based classification methodologies Saturation Analysis (SAN) and Saturation Peak Analysis (SPA) by 1/5% and 1/6% in accuracy/F1-Score metrics. We can employ our enhancement method in replacement of RGB images for classifier training e.g., CNN architectures, which in turn can lead to more accurate classification. Code will be released for public use.nb_NO
dc.language.isoengnb_NO
dc.publisherSociety for Imaging Science and Technologynb_NO
dc.titleCan Image Enhancement be Beneficial to Find Smoke Images in Laparoscopic Surgery?nb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionsubmittedVersionnb_NO
dc.source.pagenumber163-168nb_NO
dc.source.journalFinal program and proceedings (Color and Imaging Conference)nb_NO
dc.identifier.doi10.2352/ISSN.2169-2629.2018.26.163
dc.identifier.cristin1660892
dc.description.localcodeReprinted with permission of IS&T: The Society for Imaging Science and Technology sole copyright owners of the , “CIC26: Twenty-sixth Color and Imaging Conference 2018.nb_NO
cristin.unitcode194,63,10,0
cristin.unitcode194,0,0,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.unitnameNorges teknisk-naturvitenskapelige universitet
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode1


Tilhørende fil(er)

Thumbnail

Denne innførselen finnes i følgende samling(er)

Vis enkel innførsel