X ray perspective view calibration method in operation navigation system
A technique of surgical navigation and calibration method, which is applied in the field of X-ray fluoroscopic image calibration in a spinal surgery navigation system, can solve the problems of limiting the functional application of a C-arm X-ray machine, affecting the flexibility of surgical navigation, and the stability to be improved, etc. To achieve the effect of convenient clinical application, difficult to estimate deformation, and reliable algorithm
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Embodiment 1
[0019] 1. X-ray fluoroscopic image acquisition and preprocessing
[0020] The X-ray fluoroscopic image is digitized by video acquisition technology, and the noise is comprehensively suppressed by the method of multi-frame averaging filtering and wavelet transform. First, the images are multi-stacked and averaged, and then the averaged image is used as the input image for wavelet transform filtering. The type of wavelet adopts the symmetric wavelet symN proposed by Daubechies, N=2, 3, . After the above series of processing, the image noise is effectively filtered out and the detail information in the original image is well preserved.
[0021] 2. Marker recognition and correction
[0022] The background information is filtered first. Using the principle of digital subtraction: set the original image as I Source , using a kernel function whose size is the diameter of the marker point to I Source Perform median filtering to obtain the background image I without marked points ...
Embodiment 2
[0085] Example 2 Collection and calibration test of clinical fluoroscopic images
[0086] The calibration target is fixed on the image intensifier of the C-type wall X-ray machine, and the proximal target surface is close to the input screen of the intensifier to collect the fluoroscopic image of the patient. Taking into account the sampling time and the movement of the patient's body, the present invention superimposes and averages the images collected within the first 0.5 seconds after the fluoroscopy pedal is released, and performs wavelet filtering;
[0087] The marking point is automatically identified, and the kernel function of the median filter is 2.5 mm; the present invention uses filtering and subtraction methods to remove the information of the non-marking point in the image. In the template matching algorithm, with 0.85 as the threshold, all points with R(i, j)>0.85 are listed as possible marked point centers, and cluster processing is performed to find the class c...
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