Barrett's Esophagus Identification Using Optimum-Path Forest

  • Computer-assisted analysis of endoscopic images can be helpful to the automatic diagnosis and classification of neoplastic lesions. Barrett's esophagus (BE) is a common type of reflux that is not straight forward 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 Barrett'sesophagus, with promising results and outperforming the well known 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-wordsthat 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.

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Metadaten
Author:Luis Antonio de SouzaORCiD, Luis Claudio Sugi Afonso, Christoph PalmORCiDGND, João P. PapaORCiD
DOI:https://doi.org/10.1109/SIBGRAPI.2017.47
Parent Title (English):Proceedings of the 30th Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T 2017), Niterói, Rio de Janeiro, Brazil, 2017, 17-20 October
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2017
Release Date:2019/12/20
GND Keyword:Speiseröhrenkrankheit; Diagnose; Maschinelles Lernen; Bilderkennung; Automatische Klassifikation
First Page:308
Last Page:314
Institutes:Fakultät Informatik und Mathematik
Regensburg Center of Biomedical Engineering - RCBE
Regensburg Medical Image Computing - ReMIC
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke