Invariant Gabor Texture Descriptors for Classification of Gastroenterology Images

• Tipo: Artigo em Revista Científica Internacional
• Data: 2012

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. 59

Páginas: 2893-2904

ISSN: 0018-9294

Editora: IEEE

Indexação

ISI Web of Knowledge - 52 Citações

Scopus - 64 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Biotecnologia ambiental

Outras Informações

ID Authenticus: P-002-549

DOI: 10.1109/tbme.2012.2212440

Abstract (EN): Automatic classification of lesions for gastroenterology imaging scenarios poses novel challenges to computer-assisted decision systems, which are mostly attributed to the dynamics of the image acquisition conditions. Such challenges demand that automatic systems are able to give robust characterizations of tissues irrespective of camera rotation, zoom, and illumination gradients when viewing the inner surface of the gastrointestinal tract. In this paper, we study the invariance properties of Gabor filters and propose a novel descriptor, the autocorrelation Gabor features (AGF). We show that our proposed AGF is invariant to scale, rotation, and illumination changes in the images. We integrate these new features in a texton framework (Texton-AGF) to classify images from two complementary gastroenterology imaging scenarios (chromoendoscopy and narrow-band imaging) broadly into three different groups: normal, precancerous, and cancerous. Results show that they compare favorably to using state-of-the-art texture descriptors for both imaging modalities.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12