Image level set segmentation method based on local gray clustering characteristics

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: 2016-12-07
NORTHEASTERN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, the traditional level set method does not have the

Method used

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  • Image level set segmentation method based on local gray clustering characteristics
  • Image level set segmentation method based on local gray clustering characteristics
  • Image level set segmentation method based on local gray clustering characteristics

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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, where the level set segmentation curve smoothing coefficient v 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 coefficient v of the level set segmentation curve smoothing term 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 invention provides an image level set segmentation method based on local gray clustering characteristics. The method comprises the steps that images to be segmented are read; linear weighting and fitting bias fields of orthogonal basis functions are used, and the weight value of each basis function is initialized; the level set function set of the images is initialized; the energy functional of image level set segmentation is established, and level set segmentation control parameters are set according to the images to be segmented; a clustering center set, the image level set function set and basis function weight column vectors are respectively updated until meeting the stop criterion for iteration so that the energy functional of iteration is obtained; the subordinating degree function of the images, i.e. the segmentation result of the images to be segmented, is constructed according to the currently updated image level set function set, and bias field estimation of the images to be segmented is obtained according to the updated basis function weight column vectors and basis function column vectors. According to the method, the adverse impacts of weak boundary, image noise and gray inconsistency on the accuracy of image segmentation can be overcome by the method so that the method has the effect of image gray correction.

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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Application Information

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IPC IPC(8): G06T7/00G06T5/00
CPCG06T5/001
Inventor 冯朝路胡扬邓寒冰赵大哲
Owner NORTHEASTERN UNIV
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