A nematode real-time automatic tracking imaging system
An imaging system and automatic tracking technology, applied in the direction of using feedback control, etc., can solve the problems of dependence on transcription reagents and light dose, inapplicable approach and avoidance behavior, and inability to accurately describe the compound function of target neurons
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Embodiment approach 1
[0037] Implementation Mode 1: Objective Function Optimization Method
[0038] After obtaining the recognized binarized nematode image, the form of the selected objective function is as follows,
[0039] E = ∫ 0 1 { E int ( v ( s ) ) + E d i s t ( v ( s ) ) } d s
[0040] Among them, v(s), s∈[0,1] represents the parametric equation of the nematode central curve. E in the above formula int Describes the continuous smoothness of the parametric curve; E dist The minimum value of is obtained at the centerline of the nematode, and describes the degree of fitti...
Embodiment approach 2
[0044] Implementation Mode 2: Shortest Distance Distribution Extremum Search Method
[0045] After obtaining the recognized binarized nematode image, first calculate the shortest distance values from all internal points of the nematode to its exterior in the image to obtain the shortest distance matrix. It can be seen from the analysis that the shortest distance value of the point on the center line reaches a maximum value in the width direction, and this feature can be extracted by acting on the Laplacian operator on the shortest distance matrix of the point on the center line, specifically as follows: After the Laplace operator, the value at the point on the center line is basically less than -0.25, while the values at the other points are all around 0. This method cooperates with other methods, such as removing edge points, adding the median filter value of the current point, etc., to extract the discrete point series on the nematode centerline well.
[0046] In the ab...
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