DocumentCode :
2928571
Title :
Segmentation for classification of gastroenterology images
Author :
Coimbra, M. ; Riaz, F. ; Areia, M. ; Silva, F. Baldaque ; Dinis-Ribeiro, M.
Author_Institution :
Dept. of Comput. Sci., Univ. do Porto, Porto, Portugal
fYear :
2010
fDate :
Aug. 31 2010-Sept. 4 2010
Firstpage :
4744
Lastpage :
4747
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, pre-cancerous 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.
Keywords :
cancer; endoscopes; feature extraction; filtering theory; image classification; image colour analysis; image segmentation; image texture; medical image processing; pattern recognition; tumours; cancer lesions; chromoendoscopy; feature extraction; filtering; gastric tissue; gastroenterology images; hue saturation histograms; image classification; image color; image segmentation; image texture; level sets; local binary patterns; mean shift; narrow-band imaging; normalized cuts; patch index; pattern recognition; Classification algorithms; Feature extraction; Gastroenterology; Image color analysis; Image segmentation; Imaging; Manuals; Algorithms; Artificial Intelligence; Diagnostic Imaging; Digestive System Neoplasms; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE
Conference_Location :
Buenos Aires
ISSN :
1557-170X
Print_ISBN :
978-1-4244-4123-5
Type :
conf
DOI :
10.1109/IEMBS.2010.5626622
Filename :
5626622
Link To Document :
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