title: Probabilistic region matching in narrow-band endoscopy for targeted optical biopsy.
abstract: Recent advances in biophotonics have enabled in-vivo, in-situ histopathology for routine clinical applications. The non-invasive nature of these optical 'biopsy' techniques, however, entails the difficulty of identifying previously visited biopsy locations, particularly for surveillance examinations. This paper presents a novel region-matching approach for narrow-band endoscopy to facilitate retargeting the optical biopsy sites. The task of matching sparse affine covariant image regions is modelled in a Markov Random Field (MRF) framework. The proposed model incorporates appearance based region similarities as well as spatial correlations of neighbouring regions. In particular, a geometric constraint that is robust to deviations in relative positioning of the detected regions is introduced. In the proposed model, the appearance and geometric constraints are evaluated in the same space (photometry), allowing for their seamless integration into the MRF objective function. The performance of the method as compared to the existing state-of-the-art is evaluated with both in-vivo and simulation datasets with varying levels of visual complexities.
authors.affiliation: Computer Aided Medical Procedures (CAMP), Technische Universität München. atasoy@cs.tum.edu
authors.forename: Selen
authors.initials: S
authors.lastname: Atasoy
authors.forename: Ben
authors.initials: B
authors.lastname: Glocker
authors.forename: Stamatia
authors.initials: S
authors.lastname: Giannarou
authors.forename: Diana
authors.initials: D
authors.lastname: Mateus
authors.forename: Alexander
authors.initials: A
authors.lastname: Meining
authors.forename: Guang-Zhong
authors.initials: GZ
authors.lastname: Yang
authors.forename: Nassir
authors.initials: N
authors.lastname: Navab
mesh_terms: D000465:Algorithms; D001706:Biopsy / Q000379:methods*; D003627:Data Interpretation, Statistical; D004724:Endoscopy / Q000379:methods*; D004947:Esophagus / Q000473:pathology*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique