The accuracy of ultrasound/computed tomography (CT) image registration is the key to ultrasound-guided intervention. Thus, the aim of this study is to address the limitation of current image similarity measures to evaluate the accuracy of ultrasound/CT image registration correctly. In this study, an ultrasound/CT image registration method based on simulated transformation optimization is presented. The approach initially preprocesses the ultrasound/CT images for registration through the tensor principal component analysis method to reduce the influence of noise on registration accuracy. Multiscale enhancement algorithm is also adopted to enhance the tubular structures of the CT images. Simulated transformation optimization based on the CT images is then provided. Afterward, given the ultrasonic imaging parameter estimates, the method captures a CT section to obtain ultrasonic images by simulation. The ultrasonic simulation is introduced into the image similarity measure, and the simulation transformation correlation measure is established. The transformation matrix is optimized by the conjugate direction acceleration algorithm to realize the fast and accurate registration of the ultrasound/CT image. Experimental results demonstrate that when Correlation of Simulation Transformation is employed as the similarity measure, the variation range of the six parameters in the transformation matrix is ±0.01, and the ultrasound/CT image registration method based on simulated transformation optimization can rapidly and accurately register ultrasound/CT images. The accurate registration of ultrasound/CT images enables the combination between real-time ultrasonic images and preoperative CT images. Hence, it has the potential to be utilized for ultrasound-guided surgical navigation in clinical practice.
Unanticipated, reactive motion of the patient during skull based tumor resective surgery is the source of the consequence that the nasal endoscopic tracking system is compelled to be recalibrated. To accommodate the calibration process with patient's movement, this paper developed a Kinect based Real-time positional calibration method for nasal endoscopic surgical navigation system. In this method, a Kinect scanner was employed as the acquisition part of the point cloud volumetric reconstruction of the patient's head during surgery. Then, a convex hull based registration algorithm aligned the real-time image of the patient head with a model built upon the CT scans performed in the preoperative preparation to dynamically calibrate the tracking system if a movement was detected. Experimental results confirmed the robustness of the proposed method, presenting a total tracking error within 1 mm under the circumstance of relatively violent motions. These results point out the tracking accuracy can be retained stably and the potential to expedite the calibration of the tracking system against strong interfering conditions, demonstrating high suitability for a wide range of surgical applications.
The pure rotational Raman lidar temperature measurement system is usually used for retrieval of atmospheric temperature according to the echo signal ratio of high and low-level quantum numbers of N2 molecules which are consistent with the exponential relationship. An effective method to detect the rotational Raman spectrum is taking a double grating monochromator. In this paper the detection principle and the structure of the dual-grating monochromator are described, with analysis of rotational Raman’s Stokes and anti-Stokes spectrums of N2 molecule, the high order and lower order quantum number of the probe spectrum are resolved, then the specific design parameters are presented. Subsequently spectral effect is simulated with Zemax software. The simulation result indicates that under the condition of the probe laser wavelength of 532nm and using double-grating spectrometer which is comprised by two blazed gratings, Raman spectrums of 529.05nm, 530.40nm, 533.77nm, 535.13nm can be separated well, and double-grating monochromator has high diffraction efficiency.
Based on Mie scattering theory, this paper introduces the basic principle of aerosol light scattering and the basic calculation method of the polarization characteristics of scattering light. The spherical aerosol model is widely applied for the convenient and simple theoretic calculation, using the scattering theory by introducing the scattering amplitude matrix to combine incident radiation with scattering light. In scattering theory, aerosol extinction parameter has very important role for improving the precision of the laser radar, remote sensing detection and so on. We have mainly discussed the relationship between the spherical particle radius and extinction coefficient, and relationship between refractive index of the particles and extinction coefficient, respectively. It is concluded that extinction coefficient as a function of particles’ radius gradually oscillate approaching to 2 with the increasing of particle radius, and extinction coefficient curves as a function of refraction index have completely symmetric. The first major maximum of extinction coefficient also has obvious changes with different particles radius or refractive index.
Airborne LiDAR, as a precise and fast earth’s surface three-dimensional (3D) measuring method, has been widely used in the past decades. It provides a new approach for acquiring road information. By analyzing the characteristics of LiDAR datasets as well as that of the road in the datasets, a morphological method has been proposed to automatically extract the road from airborne LiDAR datasets. Firstly, ground points are segmented from raw LiDAR data by morphological operations. The key factor in this process is how to select the window sizes in different scale spaces, and setting the elevation threshold to prevent over-segmentation in each scale space. Secondly, candidate road points are segmented from the ground points, which are obtained from previous step, by intensity constraint, local point density and region area constraint, and so on. Thirdly, morphological opening operation and closing operation were used to process the candidate road points segmented from above steps. The opening operation may effectively filter the noise areas, and greatly maintain the road detail. The closing operation may fill the small holes within the road, connecting nearby roads, and smoothing the road boundary, without signification area change. The main road can be extracted from the raw airborne LiDAR points by previous three steps. Finally, the proposed method has been verified by LiDAR data which consists of complex road networks. The result shows that the proposed method can automatically extract road from airborne LiDAR data with higher efficiency and precision.