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Barrett’s Esophagus Analysis Using SURF Features

  • Luis Souza
  • Christian Hook
  • João P. Papa
  • Christoph Palm
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

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.

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Copyright information

© Springer-Verlag GmbH Deutschland 2017

Authors and Affiliations

  • Luis Souza
    • 1
    • 2
  • Christian Hook
    • 2
  • João P. Papa
    • 1
  • Christoph Palm
    • 2
    • 3
  1. 1.Faculty of Sciences, São Paulo State UniversityDepartment of ComputingSão PauloBrazil
  2. 2.Ostbayerische Technische Hochschule Regensburg (OTH Regensburg)Regensburg Medical Image Computing (ReMIC)RegensburgDeutschland
  3. 3.Regensburg Center of Biomedical Engineering (RCBE),OTH Regensburg and Regensburg UniversityRegensburgDeutschland

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