Sequential image analysis for computer-aided wireless endoscopy

dc.contributor
Universitat de Barcelona. Departament de Matemàtica Aplicada i Anàlisi
dc.contributor.author
Drozdzal, Michal
dc.date.accessioned
2014-06-25T10:08:37Z
dc.date.available
2014-06-25T10:08:37Z
dc.date.issued
2014-05-16
dc.identifier.uri
http://hdl.handle.net/10803/145614
dc.description.abstract
Wireless Capsule Endoscopy (WCE) is a technique for inner-visualization of the entire small intestine and, thus, offers an interesting perspective on intestinal motility. The two major drawbacks of this technique are: 1) huge amount of data acquired by WCE makes the motility analysis tedious and 2) since the capsule is the first tool that offers complete inner-visualization of the small intestine, the exact importance of the observed events is still an open issue. Therefore, in this thesis, a novel computer-aided system for intestinal motility analysis is presented. The goal of the system is to provide an easily-comprehensible visual description of motility-related intestinal events to a physician. In order to do it, several tools based either on computer vision concepts or on machine learning techniques are presented. A method for transforming 3D video signal to a holistic image of intestinal motility, called motility bar, is proposed. The method calculates the optimal mapping from video into image from the intestinal motility point of view. To characterize intestinal motility, methods for automatic extraction of motility information from WCE are presented. Two of them are based on the motility bar and two of them are based on frame-per-frame analysis. In particular, four algorithms dealing with the problems of intestinal contraction detection, lumen size estimation, intestinal content characterization and wrinkle frame detection are proposed and validated. The results of the algorithms are converted into sequential features using an online statistical test. This test is designed to work with multivariate data streams. To this end, we propose a novel formulation of concentration inequality that is introduced into a robust adaptive windowing algorithm for multivariate data streams. The algorithm is used to obtain robust representation of segments with constant intestinal motility activity. The obtained sequential features are shown to be discriminative in the problem of abnormal motility characterization. Finally, we tackle the problem of efficient labeling. To this end, we incorporate active learning concepts to the problems present in WCE data and propose two approaches. The first one is based the concepts of sequential learning and the second one adapts the partition-based active learning to an error-free labeling scheme. All these steps are sufficient to provide an extensive visual description of intestinal motility that can be used by an expert as decision support system.
eng
dc.format.extent
183 p.
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dc.format.mimetype
application/pdf
dc.language.iso
eng
cat
dc.publisher
Universitat de Barcelona
dc.rights.license
L'accés als continguts d'aquesta tesi queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons: http://creativecommons.org/licenses/by-nc-sa/3.0/es/
dc.rights.uri
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
*
dc.source
TDX (Tesis Doctorals en Xarxa)
dc.subject
Endoscòpia
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dc.subject
Endoscopia
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dc.subject
Endoscopy
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dc.subject
Càpsula endoscòpica
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dc.subject
Cápsula endoscópica
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dc.subject
Capsule endoscopy
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dc.subject
Motilitat gastrointestinal
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dc.subject
Motilidad gastrointestinal
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dc.subject
Gastrointestinal motility
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dc.subject
Visió per ordinador
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dc.subject
Visión por ordenador
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dc.subject
Computer vision
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dc.subject
Aprenentatge automàtic
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dc.subject
Aprendizaje automático
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dc.subject
Machine learning
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dc.subject.other
Ciències Experimentals i Matemàtiques
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dc.title
Sequential image analysis for computer-aided wireless endoscopy
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dc.type
info:eu-repo/semantics/doctoralThesis
dc.type
info:eu-repo/semantics/publishedVersion
dc.subject.udc
51
cat
dc.contributor.director
Radeva, Petia
dc.embargo.terms
cap
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dc.rights.accessLevel
info:eu-repo/semantics/openAccess
dc.identifier.dl
B 15895-2014
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