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.