@InProceedings{JúniorAfonPalmPapa:2017:BaEsId,
author = "J{\'u}nior, Luis Antonio de Souza and Afonso, Luis Cl{\'a}udio
Sugi and Palm, Christoph and Papa, Jo{\~a}o Paulo",
affiliation = "{Federal University of S{\~a}o Carlos - UFScar} and {Federal
University of S{\~a}o Carlos - UFScar} and {Ostbayerische
Technische Hochschule Regensburg} and {S{\~a}o Paulo State
University - UNESP}",
title = "Barrett’s Esophagus Identification Using Optimum-Path Forest",
booktitle = "Proceedings...",
year = "2017",
editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and
Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and
Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba,
Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo
and Vital, Creto and Pagot, Christian Azambuja and Petronetto,
Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "barrett's esophagus, machine learning, pattern recognition.",
abstract = "Computer-assisted analysis of endoscopic images can be helpful to
the automatic diagnosis and classification of neoplastic lesions.
Barretts esophagus (BE) is a common type of reflux that is not
straightforward to be detected by endoscopic surveillance, thus
being way susceptible to erroneous diagnosis, which can cause
cancer when not treated properly. In this work, we introduce the
Optimum-Path Forest (OPF) classifier to the task of automatic
identification of Barretts esophagus, with promising results and
outperforming the wellknown Support Vector Machines (SVM) in the
aforementioned context. We consider describing endoscopic images
by means of feature extractors based on key point information,
such as the Speeded up Robust Features (SURF) and Scale-Invariant
Feature Transform (SIFT), for further designing a
bag-of-visual-words that is used to feed both OPF and SVM
classifiers. The best results were obtained by means of the OPF
classifier for both feature extractors, with values lying on 0.732
(SURF) - 0.735 (SIFT) for sensitivity, 0.782 (SURF) - 0.806 (SIFT)
for specificity, and 0.738 (SURF) - 0.732 (SIFT) for the
accuracy.",
conference-location = "Niter{\'o}i, RJ, Brazil",
conference-year = "17-20 Oct. 2017",
doi = "10.1109/SIBGRAPI.2017.47",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.47",
language = "en",
ibi = "8JMKD3MGPAW/3PF8RQE",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PF8RQE",
targetfile = "PID4956031.pdf",
urlaccessdate = "2024, Apr. 28"
}