Respiratory motion tracking has been issues for MR/CT imaging and noninvasive surgery such as HIFU and
radiotherapy treatment when we apply these imaging or therapy technologies to moving organs such as liver, kidney or
pancreas. Currently, some bulky and burdensome devices are placed externally on skin to estimate respiratory motion of
an organ. It estimates organ motion indirectly using skin motion, not directly using organ itself. In this paper, we propose
a system that measures directly the motion of organ itself only using ultrasound image. Our system has automatically
selected a window in image sequences, called feature window, which is able to measure respiratory motion robustly even
to noisy ultrasound images. The organ's displacement on each ultrasound image has been directly calculated through the
feature window. It is very convenient to use since it exploits a conventional ultrasound probe. In this paper, we show that
our proposed method can robustly extract respiratory motion signal with regardless of reference frame. It is superior to
other image based method such as Mutual Information (MI) or Correlation Coefficient (CC). They are sensitive to what
the reference frame is selected. Furthermore, our proposed method gives us clear information of the phase of respiratory
cycle such as during inspiration or expiration and so on since it calculate not similarity measurement like MI or CC but
actual organ's displacement.
Recently a Time-of-Flight 2D/3D image sensor has been developed, which is able to capture a perfectly aligned
pair of a color and a depth image. To increase the sensitivity to infrared light, the sensor electrically combines
multiple adjacent pixels into a depth pixel at the expense of depth image resolution. To restore the resolution
we propose a depth image super-resolution method that uses a high-resolution color image aligned with an input
depth image. In the first part of our method, the input depth image is interpolated into the scale of the color
image, and our discrete optimization converts the interpolated depth image into a high-resolution disparity image,
whose discontinuities precisely coincide with object boundaries. Subsequently, a discontinuity-preserving filter is
applied to the interpolated depth image, where the discontinuities are cloned from the high-resolution disparity
image. Meanwhile, our unique way of enforcing the depth reconstruction constraint gives a high-resolution depth
image that is perfectly consistent with its original input depth image. We show the effectiveness of the proposed
method both quantitatively and qualitatively, comparing the proposed method with two existing methods. The
experimental results demonstrate that the proposed method gives sharp high-resolution depth images with less
error than the two methods for scale factors of 2, 4, and 8.
This paper presents a novel Time-of-Flight (ToF) depth denoising algorithm based on parametric noise modeling.
ToF depth image includes space varying noise which is related to IR intensity value at each pixel. By assuming
ToF depth noise as additive white Gaussian noise, ToF depth noise can be modeled by using a power function
of IR intensity. Meanwhile, nonlocal means filter is popularly used as an edge-preserving denoising method
for removing additive Gaussian noise. To remove space varying depth noise, we propose an adaptive nonlocal
means filtering. According to the estimated noise, the search window and weighting coefficient are adaptively
determined at each pixel so that pixels with large noise variance are strongly filtered and pixels with small
noise variance are weakly filtered. Experimental results demonstrate that the proposed algorithm provides good
denoising performance while preserving details or edges compared to the typical nonlocal means filtering.
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