Cell image segmentation method based on particle swarm neural network

Abstract

The invention discloses a cell image segmentation method based on a particle swarm neural network. The method comprises following steps of inputting 3*3 windows to replace a traditional single pixel channel; adopting a method based on information entropy to determine the quantity of hidden layer neural elements; selecting activation functions of each layer; simplifying weight training to be an optimization problem; using an improved particle swarm to optimize and solve the problem; carrying out a network training; and analyzing test results. According to the invention, the method is improved based on the traditional BP neural network, so cell images can be well segmented; problems about how to determine the network structure and how to ensure that the network is converged to be overall optimal are solved; segmentation effects of the cell images are improved; and time loss in the cell segmentation process is relatively reduced.

Description

technical field [0001] The invention relates to biomedicine and image processing technology, in particular to a cell image segmentation method based on particle swarm neural network. Background technique [0002] Cell image is an important auxiliary research method for cell embryology and pathology, and plays an important role in the research of wound healing, autonomous defense mechanism, cancer cell metastasis mechanism, blood cell and immune cell statistical analysis, etc. The segmentation of cell images has important clinical application value and can improve the diagnosis and treatment of diseases. The segmentation of cell images is a key technology in the entire image processing and analysis, and the quality of segmentation has a very important impact on subsequent diagnosis. [0003] Although there are many segmentation methods for cell images, the existing methods have more or less defects. For example, the traditional setting of the number of neurons in the hidden l...

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Problems solved by technology

[0003] Although there are many segmentation methods for cell images, the existing methods have more or less defects. For example, the traditional setting of the number of neurons in the hidden layer is not only extremely time-consuming but also has many limitations, and it also affects the neurons in the hidden layer. The number of elements is only the number of samples in the training set, the signal-to-noise ratio of the samples, the complexity of the function to be fitted, the number of classes to be classified, etc. Traditionally, the use of neural networks to segment images uses a single corresponding pixel value as the neural network. At this time, the role of the neural network is only equivalent to the "threshold method", and the classification effect is poor.

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