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Image segmentation method based on lattice Boltzman model

An image segmentation and lattice technology, applied in the field of image processing, can solve the problems of complex partial differential equation solution, small algorithm iteration step size, stability limitation, etc.

Inactive Publication Date: 2011-08-24
SHANGHAI UNIV
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  • Application Information

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Problems solved by technology

On the one hand, the partial differential equation is complex to solve, or even does not exist at all; on the other hand, due to the limitation of stability, the iteration step size of the algorithm is very small, and many iterations are required to achieve the expected effect, and the algorithm implementation process is inefficient

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  • Image segmentation method based on lattice Boltzman model
  • Image segmentation method based on lattice Boltzman model

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Embodiment 1

[0063] Example 1: Partial image segmentation

[0064] The segmentation effect of the grayscale image is as follows Figure 5 , 6 and 7, Figure 5 is the head MRI image to be segmented, with a size of 388×388; Figure 6 The initial curve set for image segmentation, where the white mark is the initial circular curve; Figure 7 It is the image segmentation effect diagram of the lattice Boltzmann model based on D2Q5.

[0065] Taking the segmentation method based on the D2Q5 lattice Boltzmann model as an example, the steps are as follows:

[0066] (1) Input initial image , the value of the node is set to the gray value of the corresponding pixel;

[0067] (2) Using the two-dimensional lattice Boltzmann model, set the initial equilibrium state function of each action direction in the lattice Boltzmann evolution equation :

[0068]

[0069] (3) Determine the number of iterations N is 80, and set the initial curve C as:

[0070]

[0071] (4) Calculate the Heavside fu...

Embodiment 2

[0082] Example 2: Global Image Segmentation

[0083] Such as Figure 8 An initial graph for setting the head MRI image segmentation to be segmented; Figure 9 It is the image segmentation effect diagram of the lattice Boltzmann model based on D2Q5. Taking the segmentation method based on the D2Q5 lattice Boltzmann model as an example, the steps are the same as in the first embodiment, and the parameter settings are changed: the initial curve set is as follows Figure 8 Shown; number of iterations N for 4 times. The final segmentation effect is as follows Figure 9 , the running time is 1.28s.

[0084] Experiments show that the image segmentation method based on Lattice Boltzmann Model can effectively perform target segmentation by setting different initial curves. For global segmentation, automatic fast segmentation can be performed, and the segmentation results are good.

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Abstract

The invention discloses an image segmentation method based on a lattice Boltzman model, which comprises the following steps: performing the segmentation process to an image based on a microscopic lattice Boltzman model, and then realizing the solution to a macroscopic partial differential equation of the image segmentation so as to realize the automatic segmentation of the image. Compared with a typical image segmentation based on a partial differential equation method, the image segmentation method can realize an iterative computation in large step so as to effectively improve the efficiencyof image segmentation with rapid segmentation speed and good segmentation effect.

Description

technical field [0001] The invention relates to an image segmentation method based on a Lattice Boltzmann Model (LBM), which belongs to the field of image processing. technical background [0002] Image segmentation refers to the technology and process of dividing the image into regions with different characteristics and proposing the target of interest. It is an important image analysis technology. In the past forty years, the research of image segmentation has been paid great attention by people. Image segmentation is an important problem in image processing and analysis, and it is also a classic problem in computer vision research. [0003] Partial differential equation methods for image processing were rapidly developed from the 1990s, showing superior performance over traditional image processing methods [1] . In the field of image processing based on partial differential equations, active contour models have been used in edge detection, Medical image segmentation a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 刘玮严壮志张蕊
Owner SHANGHAI UNIV
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