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利用最大熵多阈值方法对三维大脑数据进行分割时,穷尽搜索法耗时长,而简单遗传算法的搜索结果又不够稳定和精确.针对该问题,提出了一种免疫遗传和模拟退火相结合的新算法来快速求解全局最大熵.与简单遗传算法相比,免疫遗传算法采用了更佳的选择操作,以确保更多不同个体被选择来保存种群的多样性,而模拟退火机制用于拉伸免疫遗传算法的适应度函数.算法给出了选择概率的一般表达式,并采用精英策略和自适应的交叉、变异机制以改善算法的收敛性.基于IDL平台的100次仿真结果表明,三维大脑数据被成功地分为:脑白质、脑灰质和脑脊液三部分,且与简单遗传算法和传统免疫遗传算法相比,本文算法在稳定性和精确性上更具优势.“,”To solve large time-consumption of the complete search (CS), and the instability and inaccurateness of the simple genetic algorithm (SGA), an effective 3D brain images segmentation procedure, utilizing optimal entropy multi-thresholding method, was proposed. Global maximum entropy for the segmentation was yielded fast by the combination of the immune genetic algorithm (IGA) and simulated annealing (SA). Compared to the SGA, the IGA constructs a better selection scheme and ensures various individuals to be selected for preserving the diversity of the population. Meanwhile, the optimal entropy function of 3D medical images is stretched by the SA to construct the new fitness function, and the general expressing form of the selection probability for IGA is also given. Furthermore, to enhance the convergence of our algorithm, the proposed method includes the elitist strategy and the adaptive crossover and mutation mechanism. Results of 100 simulations demonstrate that the 3D brain volume can be successfully classified into three parts: the white matter, the gray matter and the cerebrospinal fluid on the IDL platform. The stability and accuracy of the algorithm, compared with the SGA and IGA, are all improved according to their performance contrasts.