Please use this identifier to cite or link to this item: https://hdl.handle.net/1822/66953

TitleAutomatic detection of small bowel tumors in wireless capsule endoscopy images using ensemble learning
Author(s)Vieira, Pedro Miguel
Freitas, Nuno Renato Azevedo
Valente, João
Vaz, A. Ismael F.
Rolanda, Carla
Lima, C. S.
KeywordsAnderson acceleration algorithm
capsule endoscopy
ensemble learning
support vector machines
fixed-point iteration
ROI selection
Issue date2020
PublisherWiley
JournalMedical Physics
Abstract(s)Purpose Wireless Capsule Endoscopy (WCE) is a minimally invasive diagnosis tool for lesion detection in the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the significant amount of acquired data leads to difficulties in the diagnosis by the physicians; which can be eased with computer assistance. This paper addresses a method for the automatic detection of tumors in WCE by using a two-step based procedure: region of interest selection and classification. Methods The first step aims to separate abnormal from normal tissue by using automatic segmentation based on a Gaussian Mixture Model (GMM). A modified version of the Anderson method for convergence acceleration of the expectation-maximization (EM) algorithm is proposed. The proposed features for both segmentation and classification are based on the CIELab color space, as a way of bypassing lightness variations, where the L component is discarded. Tissue variability among subjects, light inhomogeneities and even intensity differences among different devices can be overcome by using simultaneously features from both regions. In the second step, an ensemble system with partition of the training data with a new training scheme is proposed. At this stage, the gating network is trained after the experts have been trained decoupling the joint maximization of both modules. The partition module is also used at the test step, leading the incoming data to the most likely expert allowing incremental adaptation by preserving data diversity. Results This algorithm outperforms others based on texture features selected from Wavelets and Curvelets transforms, classified by a regular support vector machine (SVM) in more than 5%. Conclusions This work shows that simpler features can outperform more elaborate ones if appropriately designed. In the current case, luminance was discarded to cope with saturated tissue, facilitating the color perception. Ensemble systems remain an open research field. In the current case, changes in both topo
TypeArticle
URIhttps://hdl.handle.net/1822/66953
DOI10.1002/mp.13709
ISSN0094-2405
e-ISSN2473-4209
Publisher versionhttps://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.13709
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em revistas internacionais / Papers in international journals
ICVS - Artigos em revistas internacionais / Papers in international journals
CMEMS - Artigos em livros de atas/Papers in proceedings

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