title: SVM-MRF segmentation of colorectal NBI endoscopic images.
abstract: In this paper we investigate a method for segmentation of colorectal Narrow Band Imaging (NBI) endoscopic images with Support Vector Machine (SVM) and Markov Random Field (MRF). SVM classifiers recognize each square patch of an NBI image and output posterior probabilities that represent how likely the given patch falls into a certain label. To prevent the spatial inconsistency between adjacent patches and encourage segmented regions to have smoother shapes, MRF is introduced by using the posterior outputs of SVMs as a unary term of MRF energy function. Segmentation results of 1191 NBI images are evaluated in experiments in which SVMs were trained with 480 trimmed NBI images and the MRF energy was minimized by an α - β swap Graph Cut.
authors.forename: Tsubasa
authors.initials: T
authors.lastname: Hirakawa
authors.forename: Tom
authors.initials: T
authors.lastname: Tamaki
authors.forename: Bisser
authors.initials: B
authors.lastname: Raytchev
authors.forename: Kazufumi
authors.initials: K
authors.lastname: Kaneda
authors.forename: Tetsushi
authors.initials: T
authors.lastname: Koide
authors.forename: Yoko
authors.initials: Y
authors.lastname: Kominami
authors.forename: Shigeto
authors.initials: S
authors.lastname: Yoshida
authors.forename: Shinji
authors.initials: S
authors.lastname: Tanaka
mesh_terms: D003106:Colon / Q000033:anatomy & histology*; D004724:Endoscopy; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008390:Markov Chains; D062048:Narrow Band Imaging / Q000379:methods*; D012007:Rectum / Q000033:anatomy & histology*; D060388:Support Vector Machine