由于3维人体图像数据量大，导致分割耗时严重；人体组织间灰度差异相对较小，致使分割效果不佳。针对上述3维分割的两大难点问题，提出了改进的模糊指数熵函数来改善分割结果，并以加权免疫遗传算法（WIGA）对阈值进行优化搜索，从而提出了一种基于改进模糊指数熵双阈值的3维图像分割优化算法。真实人体胸部数据的分割结果表明，与传统熵函数及模糊隶属度函数相比，改进的最大模糊指数熵函数得到的阈值分割效果更好，且提出的WIGA算法的耗时仅为传统穷尽搜索法的14%。在与简单遗传算法（SGA）和免疫遗传算法（IGA）耗时基本相同的情况下，100次阈值计算结果表明，本文算法更加精确、稳定。

There are two important and arduous problems in 3D human images segmentation. One of them is that the large amount of data volume makes it extremely time consuming. The other is that it is difficult to segment some organs and tissues because the differences of their gray levels are relatively small. This paper proposes an optimization algorithm of 3D segmentation with two thresholds based on improved fuzzy exponential entropy by modifying the maximum fuzzy exponential entropy function, which makes the segmentation much better, and searching the optimal thresholds using the Weighting Immune Genetic Algorithm (WIGA). The experiments on the real thoracic data showed the maximum fuzzy exponential entropy function in this paper obtained better thresholds than the traditional entropy function and the fuzzy function. The searching time of WIGA is about 14 per cent of the complete searching time. Moreover, 100 calculations for thresholds showed the optimization algorithm in this paper was more precise and stable compared with the Simple Genetic Algorithm (SGA) and the Immune Genetic Algorithm (IGA) without increasing the time consumption.