Cervical lymph node ultrasonoscopy feature selection method

Abstract

The invention discloses a cervical lymph node ultrasonoscopy feature selection method, and relates to the technical field of image processing. The technical problem needing to be solved is that the genetic algorithm and the dispersing particle swarm optimization are easily caught in the local extremum when the genetic algorithm and the dispersing particle swarm optimization are utilized for carrying out cervical lymph node ultrasonoscopy feature selection. According to the technical scheme, the method comprises the first step of collecting and extracting the quantization feature of a cervical lymph node image, the second step of forming a sample set, the third step of designing an extremity learning machine classifier, the fourth step of learning and training the sample set, the fifth step of calculating the accelerated speed of each individual, the sixth step of updating the respective speed, the seventh step of calculating the respective Boltzmann function, the eighth step of updating the respective position and the ninth step of terminating the process and outputting the optimal position, namely, the optimal character subset if the end condition is met, or otherwise, adding iteration with 1 and carrying out the third step to continue evolving the population until the end condition is met. The cervical lymph node ultrasonoscopy feature selection method is suitable for helping doctors to select valuable cervical lymph node ultrasonoscopy features.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a feature selection method for lymph node ultrasound images based on a gravitational search algorithm and a Boltzmann function. Background technique [0002] Lymph nodes are distributed in the neck, armpits, groin and other parts of the entire human body. Whether the human immune system can function normally or not, lymph nodes play an important role. Clinically, pathological changes of lymph nodes are an important criterion for malignant tumors. Most clinicians use ultrasound images of cervical lymph nodes to diagnose and examine lymph node diseases. Ultrasound images of cervical lymph nodes have many lymph node characteristics, but some of these characteristics data are redundant. Therefore, selecting the features that are valuable to doctors is one of the focuses of clinicians and researchers. Feature selection is to select valuable features for classification, remov...

AI Technical Summary

Problems solved by technology

[0003] The present invention overcomes the deficiencies in the prior art, and the technical problem to be solved is to provide a feature selection method for ultrasonic images of cervical lymph nodes, which avoids the difficulty of using genetic algorithm and discrete particle swarm optimization algorithm for feature selection of ultrasonic images of cervical lymph nodes. The problem of getting stuck in a local extremum

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