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Grayscale threshold acquisition method based on adaptive genetic algorithm and image segmentation method

A gray threshold and genetic algorithm technology, applied in the field of image processing, can solve problems such as complex algorithms, achieve accurate image analysis, solve the adaptability of prior knowledge, and optimize the effect of segmentation

Active Publication Date: 2017-11-24
广东唯仁医疗科技有限公司
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Problems solved by technology

This image segmentation method combines BP neural network technology and adaptive genetic algorithm technology for image segmentation, but the algorithm is complex

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  • Grayscale threshold acquisition method based on adaptive genetic algorithm and image segmentation method
  • Grayscale threshold acquisition method based on adaptive genetic algorithm and image segmentation method
  • Grayscale threshold acquisition method based on adaptive genetic algorithm and image segmentation method

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Embodiment Construction

[0040] The following are specific embodiments of the present invention in conjunction with the accompanying drawings to further describe the technical solutions of the present invention, but the present invention is not limited to these embodiments.

[0041] Parallel region segmentation technology is a technology that uses parallel methods to detect regions of interest to segment images. First of all, for a grayscale image, the technology will classify all pixels into two categories according to a predetermined grayscale threshold in the grayscale value range of the image, and pixels with grayscale values ​​greater than the grayscale threshold are classified into one category. Pixels with a gray value less than the gray threshold are classified into another category, and pixels with a gray value equal to the gray threshold can be classified into either of the previous two categories. Generally, the two types of pixels belong to the two types of regions in the image, so that the i...

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Abstract

The invention provides a grayscale threshold acquisition method based on an adaptive genetic algorithm and an image segmentation method, and belongs to the technical field of image processing. The grayscale threshold acquisition method comprises the following steps that S01, population initialization is performed on an image grayscale value; S02, the fitness value of individuals in the population is calculated; S03, selection operation is performed and the population is updated; S04, the crossover probability of the individuals is calculated, crossover operation is performed according to the crossover probability and the population is updated; S05, the mutation probability of the individuals is calculated, mutation operation is performed according to the mutation probability and the population is updated; and S06, whether the termination condition is met is judged, and the optimal solution is obtained and the optimal grayscale threshold is obtained; or the step S02 is performed. According to the image segmentation method, image segmentation is performed according to the grayscale threshold obtained by the grayscale threshold acquisition method. The grayscale threshold acquisition method has autonomous learning and adaptability and high robustness and can solve the grayscale threshold from global concurrency so that the local optimum can be greatly avoided and the method is accurate and efficient.

Description

Technical field [0001] The technical field of image processing of the present invention, in particular, relates to a gray threshold acquisition method and an image segmentation method based on an adaptive genetic algorithm. Background technique [0002] Image processing is essentially the process of processing image information to meet people's visual psychology or application needs. And image segmentation is a kind of image processing technology, its purpose is to divide the image into areas with different characteristics and extract the interesting part to meet people's needs. In recent years, the research on image segmentation has always been a hot spot in the image processing technology research center. People are paying more and more attention to it. It is an important image analysis technology and a key step in image analysis from image processing. [0003] Image segmentation methods mainly include edge detection segmentation method, area segmentation method, threshold segme...

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Application Information

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IPC IPC(8): G06T7/11G06T7/136G06N3/12
CPCG06N3/126G06T7/11G06T7/136
Inventor 李鹏
Owner 广东唯仁医疗科技有限公司
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