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Invariant Gabor Texture Descriptors for Classification of Gastroenterology Images

Title
Invariant Gabor Texture Descriptors for Classification of Gastroenterology Images
Type
Article in International Scientific Journal
Year
2012
Authors
Farhan Riaz
(Author)
Other
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Francisco Baldaque Silva
(Author)
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Mario Dinis Ribeiro
(Author)
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Miguel Tavares Coimbra
(Author)
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Journal
Vol. 59
Pages: 2893-2904
ISSN: 0018-9294
Publisher: IEEE
Scientific classification
FOS: Engineering and technology > Environmental biotechnology
Other information
Authenticus ID: P-002-549
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.
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 12
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