基于互信息量的寄生虫卵图像自动优化分割  

Unsupervised segmentation of parasite egg image based on mutual information

作　　者：卢振泰[1] 陈武凡[1] 吕庆文[1] 

机构地区：[1]南方医科大学医学图像处理重点实验室,广州510515

出　　处：《计算机应用研究》2007年第11期301-302,共2页Application Research of Computers

基　　金：国家"973"计划资助项目(2003CB716101)

摘　　要：从分割图像与原图像的内在联系出发,提出了一种新的基于Otsu算法与互信息量技术相结合的分割算法——OMI算法。首先利用Otsu算法确定全局阈值作为初值,以互信息量为目标函数,在小范围内计算分割图像与原图像的互信息量,互信息量达到最大时的阈值即为最优值,这是将图像配准方法用于分割的一种创新性尝试。对大量人体寄生虫显微图像进行了实验,结果表明,本算法所得到的目标图像的边界特征保持完好,虚假目标信息大大降低,图像边界细腻、连续且定位性能好。Otsu algorithm was combined with mutual information （MI） technique. The initial threshold could be chosen by using Otsu algorithm, and in the iteration process, an optimal threshold was determined by maximizing the MI between the original image and the segmented image. The effectiveness of the proposed approach was evaluated by applying it to the microphotographic. The experimental results indicate that the proposed method does not only visually better or comparable segmentation effect but also, more favorably, removal ability for noise.

关 键 词：图像分割 阈值化 互信息量 OTSU算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]