title: Computer-aided colorectal tumor classification in NBI endoscopy using local features.
abstract: An early detection of colorectal cancer through colorectal endoscopy is important and widely used in hospitals as a standard medical procedure. During colonoscopy, the lesions of colorectal tumors on the colon surface are visually inspected by a Narrow Band Imaging (NBI) zoom-videoendoscope. By using the visual appearance of colorectal tumors in endoscopic images, histological diagnosis is presumed based on classification schemes for NBI magnification findings. In this paper, we report on the performance of a recognition system for classifying NBI images of colorectal tumors into three types (A, B, and C3) based on the NBI magnification findings. To deal with the problem of computer-aided classification of NBI images, we explore a local feature-based recognition method, bag-of-visual-words (BoW), and provide extensive experiments on a variety of technical aspects. The proposed prototype system, used in the experiments, consists of a bag-of-visual-words representation of local features followed by Support Vector Machine (SVM) classifiers. A number of local features are extracted by using sampling schemes such as Difference-of-Gaussians and grid sampling. In addition, in this paper we propose a new combination of local features and sampling schemes. Extensive experiments with varying the parameters for each component are carried out, for the performance of the system is usually affected by those parameters, e.g. the sampling strategy for the local features, the representation of the local feature histograms, the kernel types of the SVM classifiers, the number of classes to be considered, etc. The recognition results are compared in terms of recognition rates, precision/recall, and F-measure for different numbers of visual words. The proposed system achieves a recognition rate of 96% for 10-fold cross validation on a real dataset of 908 NBI images collected during actual colonoscopy, and 93% for a separate test dataset.
authors.affiliation: Department of Information Engineering, Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-hiroshima, Hiroshima 739-8527, Japan. tamaki@hiroshima-u.ac.jp
authors.forename: Toru
authors.initials: T
authors.lastname: Tamaki
authors.forename: Junki
authors.initials: J
authors.lastname: Yoshimuta
authors.forename: Misato
authors.initials: M
authors.lastname: Kawakami
authors.forename: Bisser
authors.initials: B
authors.lastname: Raytchev
authors.forename: Kazufumi
authors.initials: K
authors.lastname: Kaneda
authors.forename: Shigeto
authors.initials: S
authors.lastname: Yoshida
authors.forename: Yoshito
authors.initials: Y
authors.lastname: Takemura
authors.forename: Keiichi
authors.initials: K
authors.lastname: Onji
authors.forename: Rie
authors.initials: R
authors.lastname: Miyaki
authors.forename: Shinji
authors.initials: S
authors.lastname: Tanaka
mesh_terms: D003113:Colonoscopy / Q000379:methods*; D015179:Colorectal Neoplasms / Q000145:classification* / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D062048:Narrow Band Imaging