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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3PJ56FE
Repositorysid.inpe.br/sibgrapi/2017/09.04.18.02
Last Update2017:09.04.18.02.01 silvinhacolella@gmail.com
Metadatasid.inpe.br/sibgrapi/2017/09.04.18.02.01
Metadata Last Update2020:02.20.22.06.47 administrator
Citation KeyColellaRitt:2017:SeLuLe
TitleSegmentation of lung and its lesions in computer tomographic images
FormatOn-line
Year2017
DateOct. 17-20, 2017
Access Date2021, Jan. 21
Number of Files1
Size1056 KiB
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Author1 Colella, Sílvia Regina Leme
2 Rittner, Letícia
Affiliation1 University of Campinas - UNICAMP
2 University of Campinas - UNICAMP
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addresssilvinhacolella@gmail.com
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Tertiary TypeMaster's or Doctoral Work
History2017-09-04 18:02:01 :: silvinhacolella@gmail.com -> administrator ::
2020-02-20 22:06:47 :: administrator -> :: 2017
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Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsmedical image segmentation, interstitial lung diseases, computer tomography.
AbstractThe purpose of this work is to propose two new automatic segmentation methods in CT images: one for the lungs and one for their lesions. The lung segmentation method uses morphological filters and the max-tree, a data structure that represents an image through its connected components. Results show that the method presented a good performance when compared to the manual segmentation and it was able to not exclude lesions located in the borders in most of the images, which is challenging when the lesions are small and disconnected located in this region. This method obtained an average Dice of 98%. The lesion segmentation method uses the image with the segmented lungs to calculate the features to train a classifier that distinguishes between normal tissue and abnormal tissue (which contains lesions). This method also presented good results as it turned out not being very sensible to parameters' choice and it obtained an average Dice of 62% for the slices with severe pathologies.
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data URLhttp://urlib.net/rep/8JMKD3MGPAW/3PJ56FE
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PJ56FE
Languageen
Target Filewtd-sibgrapi-2017-SilviaColella-camera-ready.pdf
User Groupsilvinhacolella@gmail.com
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PJT9LS
8JMKD3MGPAW/3PKCC58
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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