Unsteady measurement algorithm based image segmentation method of improved rule distance level set

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...

AI Technical Summary

Problems solved by technology

[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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