胶囊内窥镜冗余图像数据自动筛除方法

Unsupervised redundant image deletion for wireless capsule endoscopy examination

针对胶囊内窥镜检查的海量图像数据,提出基于归一化互信息量及归一化互相关系数的冗余图像数据筛除方法。将图像在HSV色彩空间量化聚类;然后计算相邻图像的相似度系数,最后根据相似筛除比例进行迭代筛除。针对49例病例,按照70%的筛除比率,实验结果得到100%的病灶数量保留率和较低的图像误删率。基于归一化互信息量冗余图像数据筛除方法能够高效准确地筛除冗余图像数据并极大地缩短阅片时间;在该算法的基础上,开发了胶囊内窥镜图像自动筛查系统,为医生判诊提供辅助和支持。

This paper proposed an unsupervised algorithm to delete the redundant WCE images,which was based on the analysis of the normalized mutual information and normalized cross-correlation coefficient between the successive frames.The algorithm firstly conducted quantification and clustering in HSV color space.Then,it calculated the similarity metrics between the successive frames.Finally,it iteratively applied deletion procedure according to the prescribed deletion rate.The pathology retaining rate,which was defined as the percentage of the remaining images bearing pathological changes from the total ones was almost 100% with very low mis-deletion rate for 70% prescribed deletion rate of 49 patients.Experimental results show that the method based on the analysis of the normalized mutual information is effective to delete redundancy images and greatly reduces diagnosis time.