Particle swarm optimization-based gene chip image segmenting method of K-means clustering algorithm

Abstract

The invention discloses a particle swarm optimization-based gene chip image segmenting method of a K-means clustering algorithm. The particle swarm optimization-based gene chip image segmenting method is characterized by comprising the following steps of: firstly, classifying all pixels of a gene chip image into K types according to the K-means clustering algorithm and searching a local optimal position by each particle in a particle swarm according to a fitness function; and secondly, updating the speed and the position value per se by the particles in the particle swarm according to an individual extreme value and the optimal value. After multiple iterations, the subgroup at the global optimal position is the clustering classified result. The invention has the advantages of simple and definite algorithm process so as to effectively avoid the situation of involving in the local optimization or empty class, high convergence rate, strong global optimal search capability, fewer parameters required to be set and adjusted, accurate and quick result classification and no interference from human factors, and is suitable for segmenting large-scale gene chip images.

Description

technical field [0001] The invention relates to an image segmentation processing method, in particular to a method for automatically segmenting a gene chip image by using a particle swarm optimization K-means clustering algorithm. Background technique [0002] Gene chip (also known as DNA chip or biochip) is a new type of practical biotechnology developed in the mid-1980s, and has become one of the hot spots in international life science research. Gene chip technology is based on the principle of hybridization, combined with the micro-manufacturing technology and molecular biology technology of the semiconductor industry, using a large number of oligonucleotides or cDNA as probes, and fixing them in a certain order or arrangement by means of high-speed robot spotting On a very small substrate such as silicon wafer, glass slide or nylon membrane, after the fluorescently labeled sample is hybridized with the DNA sequence on the chip according to the principle of base pairing, ...

AI Technical Summary

Problems solved by technology

However, in the initialization of the class center, if the choice is not appropriate, it may fall into a local optimum or produce an empty class.

At the same time, different clustering results may be generated for different initializations, which is not conducive to the analysis and use of clustering results

In addition, the K-means clustering algorithm is greatly affected by noise and outliers

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