Impact of Visual Features on the Segmentation of Gastroenterology Images Using Normalized Cuts

• Tipo: Artigo em Revista Científica Internacional
• Data: 2013

Autores

Farhan Riaz (Autor) Outra; Francisco Baldaque Silva (Autor) Outra; Mario Dinis Ribeiro (Autor) Outra; Miguel Tavares Coimbra (Autor) FCUP

Revista

Título: IEEE Transactions on Biomedical Engineering

Vol. 60

Páginas: 1191-1201

ISSN: 0018-9294

Editora: IEEE

Indexação

ISI Web of Knowledge - 15 Citações

Scopus - 21 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Biotecnologia ambiental

Outras Informações

ID Authenticus: P-005-182

DOI: 10.1109/tbme.2012.2230174

Abstract (EN): Gastroenterology imaging is an essential tool to detect gastrointestinal cancer in patients. Computer-assisted diagnosis is desirable to help us improve the reliability of this detection. However, traditional computer vision methodologies, mainly segmentation, do not translate well to the specific visual characteristics of a gastroenterology imaging scenario. In this paper, we propose a novel method for the segmentation of gastroenterology images from two distinct imaging modalities and organs: chromoendoscopy (CH) and narrow-band imaging (NBI) from stomach and esophagus, respectively. We have used various visual features individually and their combinations (edgemaps, creaseness, and color) in normalized cuts image segmentation framework to segment ground truth datasets of 142 CH and 224 NBI images. Experiments show that an integration of edgemaps and creaseness in normalized cuts gives the best segmentation performance resulting in high-quality segmentations of the gastroenterology images.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11