Tamura Yuki, Mashita Tomohiro, Kuroda Yoshihiro, Kiyokawa Kiyoshi, Takemura Haruo
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.
Int J Comput Assist Radiol Surg. 2016 Dec;11(12):2173-2183. doi: 10.1007/s11548-016-1458-4. Epub 2016 Jul 14.
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.
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.
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.
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.
在过去十年中，增强现实系统一直被期望通过使人们能够查看位于颅骨、手部或器官内部或被其遮挡的不可见物体来支持外科手术。然而，生物组织具有非刚性且无特征的特性，这就需要大量分布的特征来详细跟踪组织的运动。
为了增加器官跟踪中的特征点数量，我们提出了一种使用多波段和窄带成像的特征检测方法以及一种新的波段选择方法。根据光学特性，光穿透物体的深度取决于光的波长。我们应用典型的特征检测器，利用在人手中选择的三个波段来检测特征点。为了考虑手术情况，我们将我们的方法应用于在各种光照条件下的鸡肝。
我们的实验结果表明，每个波段的图像呈现出不同的特征点分布。此外，所提出的方法确定的特征点总数超过了使用普通相机获得的R、G和B图像的特征点总数。使用具有各种光源和强度的鸡肝的结果也显示出每个选定波段的不同分布。
我们提出了一种使用多波段和窄带成像的特征检测方法以及一种波段选择方法。我们的实验结果证实，所提出的方法增加了分布特征点的数量。所提出的方法在不同光照条件下也有效。
