Method for automatic freezing of digestive endoscopy image based on perceptual hash algorithm
A perceptual hash algorithm and digestive endoscopy technology, applied in endoscopy, gastroscopy, computing, etc., can solve problems such as field of view deviation, image is not a doctor, and slow response speed, so as to avoid field of view deviation and reduce the work of doctors volume effect
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Embodiment 1
[0032] S1, obtain the video stream of endoscopic examination through the endoscopic examination equipment, parse the video stream into pictures (30 frames per second), remove blurred and invalid frame pictures, and take 10 frames of pictures;
[0033] S2, cutting the effective frame picture to a size of 360*360 pixels, further reducing the picture, and only retaining the structural information of the picture;
[0034] A 360*360 picture has more than 100,000 pixels, which contains a huge amount of information, and a lot of details need to be processed. Therefore, we need to zoom the picture to a very small size. The function is to remove the details of the picture, retain only the basic information such as structure, light and shade, and discard the picture differences caused by different sizes and ratios.
[0035] The bi-cubic interpolation method is used for zooming pictures. Although the amount of calculation is large, the image quality after zooming is high and it is not ea...
Embodiment 2
[0046] The difference technology with embodiment one is as follows:
[0047] In step S2, the similarity between different pictures is calculated by calculating the Hamming distance between different pictures. The Hamming distance between different pictures indicates the number of digits that need to be modified to modify the dHash value corresponding to picture A to the dHash corresponding to picture B. The formula for calculating the similarity between the current picture and the previous n frames is:
[0048] Sim=100*(64-d(x,y)) / 64;
[0049] Among them, d(x, y) represents the Hamming distance between different pictures, d(x, y)=Σx⊕y, x and y represent the dHash values corresponding to different pictures, and ⊕ represents XOR.
[0050]Calculate the Hamming distance between different pictures;
[0051] The Hamming distance indicates the number of different characters in the corresponding positions of two equal-length strings. The Hamming distance in dHash is to take the X...
Embodiment 3
[0056] The difference technology with embodiment two is as follows:
[0057] By analyzing the video of the endoscopist manually freezing the image during the endoscopic examination, the boundary line l of the weighted similarity when the image is judged to be frozen is set;
[0058] Weight the similarity of the picture at time point t Compared with the threshold l, when When , it is judged that the image is frozen at the time point t, and the image freezing instruction is triggered at the time point t; when , it is judged that the time point t is not a frozen image, then the freeze image instruction is not triggered at the time point t, and the above steps are repeated at the next time point (t+1).
[0059] Using this technical solution to replace the operation of manually freezing images can not only effectively obtain a clear image of the best field of view, but also reduce the workload of endoscopists. Its core lies in how to trigger the freeze command. Based on the ...
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