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An Image Level Set Segmentation Method Based on Local Gray Clustering Features
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A level set segmentation and local grayscale technology, applied in image analysis, image enhancement, image data processing, etc., can solve the problem of inconsistency of image grayscale
Active Publication Date: 2019-02-05
NORTHEASTERN UNIV LIAONING
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However, the traditional level set method does not have the ability to deal with the inconsistency of image gray levels
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Embodiment 1
[0063] An image level set segmentation method based on local gray-level clustering features, such as figure 1 shown, including the following steps:
[0064] Step 1: Read the image I to be segmented.
[0065] Step 2: Use the linear weighted sum of M orthogonal basis functions to fit the offset field b, and initialize the weight values of each basis function.
[0066] In this embodiment, M is 15, and the formula of the offset field b(x) is shown in formula (1):
[0067] b=w T G (1)
[0068] Among them, w=(w 1 ,w 2 ,...,w M ) T is the basis function weight column vector, T is the transpose operation symbol, G=(g 1 , g 2 ,..., g M ) T is a column vector composed of M basis functions, g 1 , g 2 ,..., g M is a pairwise orthogonal 4th-order Legendre polynomial function, w 1 =w 2 =...=w M =1 is the weight value of each basis function initialized.
[0069] Step 3: Initialize the level set function set of the image: according to the number N of regions to be divided ...
Embodiment 2
[0096] In the simplest case of the present invention, the image is divided into two regions N=2, and k=1 is calculated according to formula (2).
[0097] Such as Figure 8 In the situation shown, (a) is the four images to be segmented and the initial zero level set segmentation curve, (b) is the segmentation result of the four images to be segmented, in which the level set segmentation curve smoothing term coefficient ν of the four images to be segmented takes values in sequence 0.1×255×255, 0.5×255×255, 0.3×255×255, 0.02×255×255.
Embodiment 3
[0099] The method of the present invention divides the real image with noise, offset field and weak boundary information, divides the image into two regions N=2, and calculates k=1 according to the formula (2). Such as Figure 9 As shown, (a) is the four images to be segmented and the initial zero level set segmentation curve, (b) is the offset field estimation of the four images to be segmented obtained by the method of the present invention, (c) is the image after four offset field corrections , (d) is the segmentation result of the four images to be segmented, where the level set segmentation curve smoothing term coefficient ν of the four images to be segmented takes values 0.0045×255×255, 0.003×255×255, 0.005×255×255, 0.01 ×255×255.
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Abstract
The present invention provides an image level set segmentation method based on local grayscale clustering features. The method reads the image to be segmented, uses the linear weighted sum of orthogonal basis functions to fit the offset field, and initializes the weight of each basis function. value, initialize the level set function set of the image, establish the energy functional of the image level set segmentation, set the level set segmentationcontrol parameters according to the image to be segmented, and update the cluster center set, the image level set function set, and the basis function weight column vector respectively. , until the iteration termination condition is met, the iterative energy functional is obtained, and the membership function of the image is constructed according to the currently updated image level set function set, that is, the segmentation result of the image to be segmented, and based on the updated basis function weight column vector and The basis function column vector obtains the offset field estimate of the image to be segmented. This method overcomes the negative effects of weak boundaries, image noise, and grayscale inconsistency on image segmentation accuracy, and plays the role of correcting the image grayscale.
Description
technical field [0001] The invention belongs to the technical field of computer vision, pattern recognition, image processing and analysis, and in particular relates to an image level set segmentation method based on local gray-scale clustering features. Background technique [0002] Image segmentation plays an important role in the fields of computer vision, pattern recognition, and image processing. Its purpose is to divide the image into multiple regions of interest, and the pixels in each region form an object of interest with specific shape and structural characteristics. Although it has been studied for many years and a variety of segmentation methods have been proposed, it is still a challenge to segment objects of interest from images with complex structures in the presence of noise and gray-scale offset fields. The level set segmentation method transforms the image segmentation into an energy minimization problem of the energy functional defined on the level set fu...
Claims
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