q Multispectral Endoscopy to Identify Precancerous Lesions in Gastric Mucosa
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | q Multispectral Endoscopy to Identify Precancerous Lesions in Gastric Mucosa |
| Type de publication | Conference Paper |
| Year of Publication | 2014 |
| Auteurs | Martinez-Herrera SE, Benezeth Y, Boffety M, Emile J-F, Marzani F, Lamarque D, Goudail F |
| Editor | Elmoataz A, Lezoray O, Nouboud F, Mammass D |
| Conference Name | IMAGE AND SIGNAL PROCESSING, ICISP 2014 |
| Publisher | European Assoc Image & Signal Proc; Int Assoc Pattern Recognit; Inst Univ Technologie; Univ Caen Basse Normandie; ENSICAEN; Ctr Natl Rech Sci; Conseil Reg Basse Normandie; Conseil Gen Manche; Communaut Urbaine Cherbourg |
| Conference Location | HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY |
| ISBN Number | 978-3-319-07998-1; 978-3-319-07997-4 |
| Mots-clés | gastroendoscopy, Multispectral imaging, Neural Networks, precancerous lesions, SVM |
| Résumé | Precancerous lesions are in many situations not visible during white light gastroendoscopy. Different approaches have been proposed based on light tissue interaction in order to improve the visualization by creating false color images. However, these systems are limited to few wavelengths. In this paper, we propose a multispectral gastroendoscopic system and a methodology to identify precancerous lesions. The multispectral images collected during gastroendoscopy are used to compute statistical features from their spectrum. Pooled variance t-test is used to rank the features in order to train 3 classifiers with different number of features. The 3 classifiers are Neural Networks using Generalized Relevance Learning Vector Quantization (GRLVQ), SVM with a Gaussian kernel and K-nn. The performance is compared based on their ability to identify precancerous lesions, using as quantitative index the accuracy, specificity and sensitivity. SVM presents the best performance, showing the effectiveness of the method. |