Feature detection in biological tissues using multi-band and narrow-band imaging

Yuki Tamura, Tomohiro Mashita, Yoshihiro Kuroda, Kiyoshi Kiyokawa, Haruo Takemura

Abstract:

Purpose: 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