Unsteady measurement algorithm based image segmentation method of improved rule distance level set
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An image segmentation and level set technology, applied in image analysis, image data processing, computing and other directions, can solve the problems of inability to meet segmentation requirements, noise sensitivity, and low segmentation accuracy.
Inactive Publication Date: 2012-12-19
HARBIN INST OF TECH
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[0027] The DRLSE model is a new image segmentation algorithm based on the level set method. Compared with the GAC model, it has the advantage of not needing to be reinitialized, but it still has the problem of being sensitive to noise.
For images disturbed by Gaussian and salt and pepper noise, the segmentation accuracy of the DRLSE model is low and cannot meet the segmentation requirements
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specific Embodiment approach 1
[0068] Specific embodiment one: this embodiment adopts the image segmentation method based on the improved regular distance level set of the non-steady-state measurement algorithm, and the experimental figure is Gaussian noise mixed with a standard deviation of 0.1. The steps to realize the method are as follows:
[0069] 1. Construct the mean operator, and use the constructed operator to set the stop function; the construction method is:
[0070] a. Construct two square matrices with side length L, and the element values in the matrix are all 1 / L 2 , that is, the mean filter h 1 and h 2 , where L generally takes 3, 5 or 9.
[0071] In this embodiment, the parameter selection L=3.
[0072] b. Use the constructed filter h 1 , h 2 , using the formula g = 1 1 + k | ( I * ...
specific Embodiment approach 2
[0097] Specific implementation mode two: the present embodiment adopts a simulated double-target graph, and then adds Gaussian noise with variances of 0.0, 0.1, 0.2, and 0.5 to the graph, thereby forming four groups of simulation data with different noise intensities;
[0098] The steps to implement this method are as follows:
[0099] 1. Construct the mean operator, and use the constructed operator to set the stop function; the construction method is:
[0100] a. Construct two square matrices with side length L, and the element values in the matrix are all 1 / L 2 , that is, the mean filter h 1 and h 2 , where L generally takes 3, 5 or 9.
[0101] In this embodiment, the parameter selection L=3.
[0102] b. Use the constructed filter h 1 , h 2 , using the formula g = 1 1 + k | ( I * ...
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Abstract
The invention discloses an unsteady measurement algorithm based image segmentation method of an improved rule distance level set, belongs to the field of digital image processing and aims to more precisely segment images of gaussian noise and salt and pepper noise interference. The method includes: firstly, constructing a mean operator, and setting a stopping function by the constructed operator; secondly, manually setting an initial contour, and initializing a level set function according to the contour; and thirdly, importing the stopping function set in the first step into an energy equation of a DRLSE model, minimizing the energy equation by the aid of a central difference method, and iterating by taking the initialized level set obtained in the second step as an initial condition to obtain a zero level set of a steady state solution, namely the final segmentation results. The method is more precise in segmentation of the images of the gaussian noise and salt and pepper noise interference as compared with a traditional geometric Snake model method.
Description
technical field [0001] The invention relates to an image segmentation method based on a Snake model, belonging to the field of digital image processing. Background technique [0002] Snake model, also known as active contour model, snake model. The meaning of the active contour is to manually or automatically set the initial contour around the target to be segmented, and to give the initial contour energy. The contour deforms under the action of the topological internal force of the model itself and the external force generated by the grayscale data of the image, and moves like a snake. When the energy of the contour reaches the minimum, the position of the contour is the edge of the target to be segmented. [0003] The Snake model is the process of transforming the image segmentation problem into the minimization of the energy functional function. The outstanding advantage of the active contour is that once the initial contour is set, the subsequent contour evolution does...
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