基于格式塔认知框架的腹腔CT多目标分割算法

为了解决腹腔软组织电子计算机断层扫描影像难于分割的难题，提出一种基于格式塔认知框架的多目标分割算法.通过借鉴格式塔认知框架中“邻近度、相似度”的思想，引入超像素算法处理电子计算机断层扫描图像处理，生成可视块.进一步地，在可视块粒度描述有向邻接关系，以软组织的相对空间位置约束聚类分割过程.在公开数据库上的实验结果表明，该算法降低了聚类的计算量，其结果比当前流行的算法准确率更高.

Multi-Object Segmentation for Abdominal CT Images Based on Gestalt Cognitive Framework

In order to acquire better organ segmentation from abdominal computed tomography ( CT) ima-ges, a multi-object segmentation algorithm based on cognitive framework is proposed. Inspired by the proximity and similarity idea in gestalt, super pixel concept of CT image processing has been produced. Specifically, by establishing the directed adjacency relationship of super-pixel, the spatial relationships of abdominal organs are modeled as prior knowledge to improve the classification accuracy. Experiments in public datasets illustrate that the proposed algorithm achieves better performance in either speed or accu-racy than that of the state-of-art methods.