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Automatic Detection of Intestinal Content to Evaluate Visibility in Capsule Endoscopy

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Automatic Detection of Intestinal Content to Evaluate Visibility in Capsule Endoscopy

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dc.contributor.author Noorda, Reinier es_ES
dc.contributor.author Nevárez, Andrea es_ES
dc.contributor.author Colomer, Adrián es_ES
dc.contributor.author Naranjo, Valery es_ES
dc.contributor.author Pons Beltrán, Vicente es_ES
dc.date.accessioned 2020-01-30T09:00:27Z
dc.date.available 2020-01-30T09:00:27Z
dc.date.issued 2020-01-30T09:00:27Z
dc.identifier.isbn 978-1-7281-2342-4
dc.identifier.issn 2326-8301
dc.identifier.uri http://hdl.handle.net/10251/136059
dc.description.abstract In capsule endoscopy (CE), preparation of the small bowel before the procedure is believed to increase visibility of the mucosa for analysis. However, there is no consensus on the best method of preparation, while comparison is difficult due to the absence of an objective automated evaluation method. The method presented here aims to fill this gap by automatically detecting regions in frames of CE videos where the mucosa is covered by bile, bubbles and remainders of food. We implemented two different machine learning techniques for supervised classification of patches: one based on hand-crafted feature extraction and Support Vector Machine classification and the other based on fine-tuning different convolutional neural network (CNN) architectures, concretely VGG-16 and VGG-19. Using a data set of approximately 40,000 image patches obtained from 35 different patients, our best model achieved an average detection accuracy of 95.15% on our test patches, which is similar to significantly more complex detection methods used for similar purposes. We then estimate the probabilities at a pixel level by interpolating the patch probabilities and extract statistics from these, both on per-frame and per-video basis, intended for comparison of different videos. es_ES
dc.description.sponsorship This work was funded by the European Union’s H2020: MSCA: ITN program for the “Wireless In-body Environment Communication – WiBEC” project under the grant agreement no. 675353. es_ES
dc.format.extent 6 es_ES
dc.language Inglés es_ES
dc.publisher IEEE es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Image processing es_ES
dc.subject Machine learning es_ES
dc.subject Support vector machines es_ES
dc.subject Local binary patterns es_ES
dc.subject Capsule endoscopy es_ES
dc.subject Small bowel preparation es_ES
dc.subject Convolutional neural networks es_ES
dc.title Automatic Detection of Intestinal Content to Evaluate Visibility in Capsule Endoscopy es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.1109/ISMICT.2019.8743878
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/675353/EU es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Telecomunicación y Aplicaciones Multimedia - Institut Universitari de Telecomunicacions i Aplicacions Multimèdia es_ES
dc.description.bibliographicCitation Noorda, R.; Nevárez, A.; Colomer, A.; Naranjo, V.; Pons Beltrán, V. (2020). Automatic Detection of Intestinal Content to Evaluate Visibility in Capsule Endoscopy. IEEE. 163-168. https://doi.org/10.1109/ISMICT.2019.8743878 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename International Symposium on Medical Information and Communication Technology (ISMICT) es_ES
dc.relation.conferencedate Mayo 08-10,2019 es_ES
dc.relation.conferenceplace Oslo, Norway es_ES
dc.relation.publisherversion https://doi.org/10.1109/ISMICT.2019.8743878 es_ES
dc.description.upvformatpinicio 163 es_ES
dc.description.upvformatpfin 168 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela S\393105 es_ES
dc.contributor.funder European Commission es_ES


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