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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Author:Luis Antonio de Souza Jr.ORCiD, Alanna EbigboORCiD, Andreas Probst, Helmut Messmann, João Paulo PapaORCiD, Robert MendelORCiD, Christoph PalmORCiDGND
Parent Title (English):31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Parana, 2018
Document Type:conference proceeding (article)
Year of first Publication:2018
Release Date:2020/04/29
Tag:Barrett’s Esophagus; Co-occurrence Matrices; Machine learning; Texture Analysis
First Page:166
Last Page:173
Institutes:Fakultät Informatik und Mathematik
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
research focus:Lebenswissenschaften und Ethik
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