title: Feature detection in biological tissues using multi-band and narrow-band imaging.
abstract: OBJECTIVE
  In the past decade, augmented reality systems have been expected to support surgical operations by making it possible to view invisible objects that are inside or occluded by the skull, hands, or organs. However, the properties of biological tissues that are non-rigid and featureless require a large number of distributed features to track the movement of tissues in detail.
  
  
  METHODS
  With the goal of increasing the number of feature points in organ tracking, we propose a feature detection using multi-band and narrow-band imaging and a new band selection method. The depth of light penetration into an object depends on the wavelength of light based on optical characteristics. We applied typical feature detectors to detect feature points using three selected bands in a human hand. To consider surgical situations, we applied our method to a chicken liver with a variety of light conditions.
  
  
  RESULTS
  Our experimental results revealed that the image of each band exhibited a different distribution of feature points. In addition, the total number of feature points determined by the proposed method exceeded that of the R, G, and B images obtained using a normal camera. The results using a chicken liver with various light sources and intensities also show different distributions with each selected band.
  
  
  CONCLUSIONS
  We have proposed a feature detection method using multi-band and narrow-band imaging and a band selection method. The results of our experiments confirmed that the proposed method increased the number of distributed feature points. The proposed method was also effective for different light conditions.
authors.affiliation: Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita City, Osaka, 565-0871, Japan. tamura.yuuki@lab.ime.cmc.osaka-u.ac.jp.
authors.forename: Yuki
authors.initials: Y
authors.lastname: Tamura
authors.affiliation: Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita City, Osaka, 565-0871, Japan.|Cybermedia Center, Osaka University, 1-32 Machikaneyamacho, Toyonaka City, Osaka, 560-0043, Japan.
authors.forename: Tomohiro
authors.initials: T
authors.lastname: Mashita
authors.affiliation: Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka City, Osaka, 560-8531, Japan.
authors.forename: Yoshihiro
authors.initials: Y
authors.lastname: Kuroda
authors.affiliation: Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita City, Osaka, 565-0871, Japan.|Cybermedia Center, Osaka University, 1-32 Machikaneyamacho, Toyonaka City, Osaka, 560-0043, Japan.
authors.forename: Kiyoshi
authors.initials: K
authors.lastname: Kiyokawa
authors.affiliation: Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita City, Osaka, 565-0871, Japan.|Cybermedia Center, Osaka University, 1-32 Machikaneyamacho, Toyonaka City, Osaka, 560-0043, Japan.
authors.forename: Haruo
authors.initials: H
authors.lastname: Takemura
mesh_terms: D000818:Animals; D002645:Chickens; D006225:Hand / Q000000981:diagnostic imaging*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008099:Liver / Q000000981:diagnostic imaging*; D062048:Narrow Band Imaging; D010363:Pattern Recognition, Automated
keywords: Augmented reality; Biological tissues; Feature detection; Multi-band imaging; Narrow-band imaging