中文摘要：

在脑光学功能成像领域，由T．Yokoo等人提出的广义指示函数法（Generalized Indicator Functions；GIF）能够在极低信噪比下有效地提取大脑行为模式图．但进一步研究发现，该算法在处理脑功能光学图像序列时存在计算量大的缺点．为解决一问题，本文将Weng等在处理FERET人脸数据库时提出的一种迭代算法与GIF算法相结合，给出了一种迭代格式的GIF算法——RGIF（Recursive GIF），RGIF算法利用迭代计算的特点能大幅削减计算量．利用仿真和实验数据对GIF和RGIF算法进行了对比分析，结果表明RGIF小仅能够大大节省计算时间，同时检测效果与GIF相当．

英文摘要：

In the analysis of optical imaging of functional brain,the generalized indicator functions （GIF） algorithm presented by T. Yokoo,etc. is an efficient method to extract the brain activity map. But further study shows that this algorithm has the shortage of heavy computation in dealing with brain image series. In order to resolve this problem, a recursive GIF （RGIF） algorithm is presented,which is the combination of Weng＇s recursive algorithm in dealing with the FERET face database and the GIF algorithm, the RGIF algorithm can sharply reduce the computation utilizing the characteristic of recursive algorithm. We compare the GIF/and RGIF algorithms using the simulated and experimental datum, the results show that the RGIF algorithm can relieve the computational burden substantially with at almost the same computing precision as that with GIF algorithm.