Abstract
The development of adenocarcinoma in Barrett’s esophagus is difficult to detect by endoscopic surveillance of patients with signs of dysplasia. Computer assisted diagnosis of endoscopic images (CAD) could therefore be most helpful in the demarcation and classification of neoplastic lesions. In this study we tested the feasibility of a CAD method based on Speeded up Robust Feature Detection (SURF). A given database containing 100 images from 39 patients served as benchmark for feature based classification models. Half of the images had previously been diagnosed by five clinical experts as being ”cancerous”, the other half as ”non-cancerous”. Cancerous image regions had been visibly delineated (masked) by the clinicians. SURF features acquired from full images as well as from masked areas were utilized for the supervised training and testing of an SVM classifier. The predictive accuracy of the developed CAD system is illustrated by sensitivity and specificity values. The results based on full image matching where 0.78 (sensitivity) and 0.82 (specificity) were achieved, while the masked region approach generated results of 0.90 and 0.95, respectively.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer-Verlag GmbH Deutschland
About this paper
Cite this paper
Souza, L., Hook, C., Papa, J.P., Palm, C. (2017). Barrett’s Esophagus Analysis Using SURF Features. In: Maier-Hein, geb. Fritzsche, K., Deserno, geb. Lehmann, T., Handels, H., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2017. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54345-0_34
Download citation
DOI: https://doi.org/10.1007/978-3-662-54345-0_34
Published:
Publisher Name: Springer Vieweg, Berlin, Heidelberg
Print ISBN: 978-3-662-54344-3
Online ISBN: 978-3-662-54345-0
eBook Packages: Computer Science and Engineering (German Language)