一种基于agent随机扩散的图像分割方法

图像分割是图像处理的关键环节,直接影响以后的分析、识别和解译。根据进化agent具有自适应性、非线性映射和高度并行处理能力等优点,提出了一种基于agent随机扩散的图像分割方法。在该方法中,agent点随机地撒在网格单元上,并在满足一致性标准的区域用标签标定。agent点有复制和扩散两种行为扩散模式,当一个agent成功的找到一个像素满足一致性标准,它将在周围区域复制一系列后代,因此这些后代更容易找到那些满足一致性条件的像素,而对于那些超过生命周期的agent点将停止搜索,从环境中消失。利用医学胸部的CT图像和脑部的磁共振图像进行的实验结果表明,该方法能较好地从图像中提出感兴趣的区域。

A Method for Image Segmentation Based on Random Diffused Agents

Image segmentation is an important process in image processing.The quality of image segmentation directly affects the following analysis and recognition.Because evolutionary agent has some advantages such as self-fitness non-linear mapping and ability of parallel disposal,this paper presents an autonomous agent-based image segmentation approach.In this approach,a digital image is viewed as a two-dimensional cellular environment in which the agents inhabit and attempt to label homogeneous segments.The agents rely on some reactive behaviors such as breeding and diffusion.The agents that are successful in finding the pixels of a specific homogeneous segment will breed offspring agents inside their neighboring regions.Hence,the offspring agents will become likely to find more homogeneous-segment pixels.In the mean time,the unsuccessful agents will be inactivated without further search in the environment.It can be seen from our experiment in medical chest CT image and brain MRI image that this method can better extract interesting regions.