Particle swarm and fuzzy means clustering based cell image segmentation method

A technology of fuzzy mean clustering and image segmentation, which is applied in image analysis, image enhancement, image data processing, etc. It can solve the problems of inaccurate cell segmentation edges, cell adhesion, and difficult cell segmentation, etc., and achieve broad market prospects and applications value effect
CN106296709AActive Publication Date: 2017-01-04BEIHANG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
BEIHANG UNIV
Publication Date
2017-01-04

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Abstract

The invention relates to a particle swarm and fuzzy means clustering based cell image segmentation method. On the basis of an intuitive fuzzy means clustering theory, an intuitive fuzzy membership degree form is improved aiming at cell images and combined with fractional order particle swarms to realize alternate optimization, and a morphological method is adopted for result improvement to realize accurate segmentation of the cell images. The method includes steps: firstly, selecting appropriate parameters, initial particle swarms and relevant data; secondly, starting alternate iterative optimization of the fractional order particle swarms and intuitive fuzzy clustering; thirdly, comparing results with standard fuzzy means clustering results, and selecting a morphological processing method through differential images; finally, subjecting result images to range conversion and watershed segmentation to obtain a final result. The particle swarm and fuzzy means clustering based cell image segmentation method can be widely applied to various cell image based application systems and has a promising market prospect and a high application value.
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Description

[0001] (1) Technical field

[0002] The invention relates to a cell image segmentation method based on particle swarm and fuzzy mean value clustering, belongs to the field of digital image processing, and mainly relates to intelligent optimization and image segmentation technology. It has broad application prospects in various application systems based on cell images.

[0003] (2) Background technology

[0004] Compared with the traditional optimization algorithm, the intelligent optimization algorithm does not need to know the mathematical characteristics of the optimal solution, but performs a heuristic algorithm on the problem to obtain the optimal solution, which itself does not necessarily obtain the actual optimal solution. But it can get an approximate solution close to the actual optimal solution in a short time, so it has been widely used. The intelligent optimization algorithm is to simulate the phenomenon of nature, and constantly adjust the strategy in the search p...

Claims

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