选择具有识别作用的超声图像淋巴结区域特征对临床诊断具有重要价值。针对目前特征选择算法收敛速度慢和容易陷入局部极小值的问题,提出病毒协同进化的离散差分进化的颈部淋巴结超声图像特征选择算法。该算法主要利用病毒感染操作进行宿主个体的变异,在维持宿主个体多样性的同时保留最优的搜索信息,提高了算法的适应度函数值和进化速度。在临床颈部淋巴结超声图像中进行实验验证,分类精度达到98%,而算法平均收敛迭代次数仅为30次,表明本文所提算法是正确有效的。

Selecting regional features in ultrasound images of lymph nodes is important for clinical diagnosis. Most of the current feature selection algorithms are time-consuming and lead easily to a premature convergence. In this paper, a new novel discrete differential evolution (DDE) algorithm based on virus-evolution is presented to solve the cervical lymph nodes features selection problem.We call it virus-evolutionary discrete differential evolution (VEDDE) algorithm. Biological virus mechanism and the infection-based operation between host and virus are introduced in the DDE which can maintain the diversity of individuals while retaining the best search information and improve the fitness function value and the speed of evolution. The proposed algorithm has been tested on many clinical ultrasound images of cervical lymph nodes. The classification accuracy is 98% and the average number of iterations is only 30 times,which indicates that the proposed algorithm is valid.