Barrett's Esophagus Identification Using Color Co-occurrence Matrices

Autor(es)

de Souza, Luis A., Jr. ; Ebigbo, Alanna ; Probst, Andreas ; Messmann, Helmut ; Papa, Joao P. ; Mendel, Robert ; Palm, Christoph ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: PROCEEDINGS 2018 31ST SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI); v. N/A, p. 8-pg., 2018-01-01.

Resumo

In this work, we propose the use of single channel Color Co-occurrence Matrices for texture description of Barrett's Esophagus (BE) and adenocarcinoma images. Further classification using supervised learning techniques, such as Optimum-Path Forest (OPF), Support Vector Machines with Radial Basis Function (SVM-RBF) and Bayesian classifier supports the context of 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 to 76%. 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. (AU)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 16/19403-6 - Modelos de aprendizado baseados em energia e suas aplicações
• Beneficiário: João Paulo Papa
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático