Barrett's Esophagus Identification Using Color Co-occurrence Matrices

  • In this work, we propose the use of single channel Color Co-occurrence Matrices for texture description of Barrett’sEsophagus (BE)and adenocarcinoma images. Further classification using supervised learning techniques, such as Optimum-Path Forest (OPF), Support Vector Machines with Radial Basisunction (SVM-RBF) and Bayesian classifier supports the contextof automatic BE and adenocarcinoma diagnosis. We validated three approaches of classification based on patches, patients and images in two datasets (MICCAI 2015 and Augsburg) using the color-and-texture descriptors and the machine learning techniques. Concerning MICCAI 2015 dataset, the best results were obtained using the blue channel for the descriptors and the supervised OPF for classification purposes in the patch-based approach, with sensitivity nearly to 73% for positive adenocarcinoma identification and specificity close to 77% for BE (non-cancerous) patch classification. Regarding the Augsburg dataset, the most accurate results were also obtained using both OPF classifier and blue channel descriptor for the feature extraction, with sensitivity close to 67% and specificity around to76%. Our work highlights new advances in the related research area and provides a promising technique that combines color and texture information, allied to three different approaches of dataset pre-processing aiming to configure robust scenarios for the classification step.

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Metadaten
Author:Luis Antonio de SouzaORCiD, Alanna EbigboORCiD, Andreas Probst, Helmut Messmann, Joao P. PapaORCiD, Robert Mendel, Christoph PalmORCiDGND
URL / DOI:http://urlib.net/rep/8JMKD3MGPAW/3RP9Q35
Parent Title (English):Conference on Graphics, Patterns and Images (Sibgrapi)
Document Type:Article
Language:English
Year of first Publication:2018
Release Date:2020/04/29
Tag:Barrett’s Esophagus; Co-occurrence Matrices; Machine learning; Texture Analysis
Volume:96
First Page:203
Last Page:213
Institutes:Fakultät Informatik und Mathematik
Regensburg Medical Image Computing - ReMIC
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke