Self-adaptive spinal CT image segmentation method based on particle swarm optimization

A particle swarm algorithm and CT image technology, applied in the field of image segmentation, can solve problems such as the improvement of segmentation accuracy and time complexity, affecting the speed and accuracy of image segmentation, and achieve improved search accuracy, improved image segmentation accuracy, and fast convergence rate Effect

Inactive Publication Date: 2016-03-16
SOUTHERN MEDICAL UNIVERSITY +1
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Problems solved by technology

However, although the particle swarm algorithm is simple to implement and has few parameters, it is easy to fall into local optimum, and the selection of parameters directly affects the speed and accuracy of image segmentation.
[0004] In "ImageThresholdSegmentationMethodBasedonImprovedParticleSwarmOptimization" (ComputerScience39(2012)

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  • Self-adaptive spinal CT image segmentation method based on particle swarm optimization
  • Self-adaptive spinal CT image segmentation method based on particle swarm optimization
  • Self-adaptive spinal CT image segmentation method based on particle swarm optimization

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[0039] The present invention will be further described below in conjunction with the accompanying drawings.

[0040] refer to figure 1, the specific implementation steps of the present invention are as follows:

[0041] Step 1, enter image 3 For the original image shown, find the minimum gray value and maximum gray value of the image.

[0042] Step 2, initialize the first-generation population; randomly generate an integer between the minimum gray value and the maximum gray value to initialize each dimension of the individual of the population; in the embodiment of the present invention, the dimension is taken as 1.

[0043] Step 3, calculate the individual optimal value and the global optimal value.

[0044] Let the image be {t _1 ,t _2 ,...,t _M-1} thresholds are divided into M parts, then the optimal segmentation threshold {t * _1 ,t * _2 ,...,t * _M-1} need to meet the following conditions:

[0045] {t * _1 ,t * _2 ,...,t * _M-1} = argmax{σ 2 (t 1 ,t ...

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Abstract

The invention relates to a self-adaptive spinal CT image segmentation method based on particle swarm optimization. The method comprises the following steps that (1) an original image is inputted; (2) the first generation of population is initialized; (3) an individual optimum and a global optimum are calculated; (4) new individuals are generated; (5) a new individual optimum and a global optimum are calculated; (6) whether the maximum number of iterations is met is judged, and the process goes to step (7) if the judgment result is yes, or the process returns to the step (4); (7) initial image segmentation is performed to act as an initial segmentation result; and (8) topology operation is adopted to perform further accurate spinal segmentation so that the image is outputted. Search granularity is optimized, and speed of algorithm convergence is controlled in a coarse-to-fine way so that speed of algorithm convergence is greatly enhanced, segmentation precision is enhanced, further accurate spinal segmentation is performed by adopting the topology through the prior knowledge of areas to be segmented, and thus image segmentation efficiency and quality are greatly enhanced.

Description

technical field [0001] The present invention relates to general image processing, specifically to image analysis, and in particular to image segmentation through analysis of image attributes. Background technique [0002] With the continuous development of computer science and technology, image processing technology has also been developed to a certain extent, and gradually formed its own scientific theory system. Image segmentation is an important technology of image processing, which has been paid more and more attention by more and more people in practical application and theoretical research. [0003] Image segmentation technology is to divide the image into several meaningful regions and extract the objects of interest so that the follow-up work can be carried out effectively. It is a key step from image processing to image analysis. The existing image segmentation methods can be divided into the following categories: segmentation methods based on regions, segmentation...

Claims

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T2207/10081G06T2207/30012
Inventor 李彭军严静东杨小燕朱旭阳
Owner SOUTHERN MEDICAL UNIVERSITY
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