Please use this identifier to cite or link to this item:
Author(s): Coimbra, M
Riaz, F
Areia, M
Baldaque Silva, FB
Dinis Ribeiro, M
Title: Segmentation for Classification of Gastroenterology Images
Issue Date: 2010
Abstract: Automatic classification of cancer lesions in tissues observed using gastroenterology imaging is a non-trivial pattern recognition task involving filtering, segmentation, feature extraction and classification. In this paper we measure the impact of a variety of segmentation algorithms (mean shift, normalized cuts, level-sets) on the automatic classification performance of gastric tissue into three classes: cancerous, precancerous and normal. Classification uses a combination of color (hue-saturation histograms) and texture (local binary patterns) features, applied to two distinct imaging modalities: chromoendoscopy and narrow-band imaging. Results show that mean-shift obtains an interesting performance for both scenarios producing low classification degradations (6%), full image classification is highly inaccurate reinforcing the importance of segmentation research for Gastroenterology, and confirm that Patch Index is an interesting measure of the classification potential of small to medium segmented regions.
Subject: Ciência de computadores, Biotecnologia ambiental
Computer science, Environmental biotechnology
Scientific areas: Ciências da engenharia e tecnologias::Biotecnologia ambiental
Engineering and technology::Environmental biotechnology
Source: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Document Type: Artigo em Livro de Atas de Conferência Internacional
Rights: restrictedAccess
Appears in Collections:FCUP - Artigo em Livro de Atas de Conferência Internacional
FMUP - Artigo em Livro de Atas de Conferência Internacional

Files in This Item:
File Description SizeFormat 
  Restricted Access
Artigo970.48 kBAdobe PDF    Request a copy from the Author(s)

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.