基于信息熵的CT图像目标自动提取实验研究——以恐龙蛋壳化石切片CT图像为例

Experimental research on automatic object extraction from CT image based on information entropy——taking CT image of dinosaur eggshell slices as an example

以恐龙蛋为对象,针对大量恐龙蛋壳化石CT图像目标与背景的分离需求,以及传统提取方法繁琐、精确度不高且需要较多人工参与,不能实现完全自动化等问题,提出了一种基于信息熵的CT图像目标自动分离提取方法。首先手动训练样本信息熵参数,将其作为自动分离大量CT图像的参数;再根据灰度图像亮度直方图确定分割阈值;然后基于信息熵算法进行自动分离;最后依据分割阈值和信息熵值,实现目标区域的最终分离和提取。该方法获得了良好的分离提取效果,所获分割阈值范围为66~188,信息熵值范围为0.43~0.65。基于3329张16位恐龙蛋壳原始切片CT图像样品数据所进行的评价实验表明,对于数量较多的CT图像,所提出的方法自动分离提取具有很高的效率,可达到98.89%,并且能在正确提取出目标方解石的同时保留较为完整的目标与边缘细节,分离处理的准确性和快速性良好。

Taking dinosaur eggs as the object,a method of automatic object separation and extraction of CT images based on information entropy is proposed in view of the separation demand of many dinosaur eggshell fossil CT images with target and background,as well as the present situation of cumbersome,low accuracy,requiring more manual participation,and unable to achieve complete automation,etc.Firstly,the sample information entropy parameters are manually trained and used as the parameters for automatic separation of a large number of CT images.Secondly,the segmentation threshold is determined according to the brightness histogram of gray image.Then,the automatic separation is performed based on information entropy algorithm.Finally,according to the segmentation threshold and information entropy value,the final separation and extraction of the target region is realized.The method achieves good separation and extraction results,the segmentation threshold range is 66~188,and the information entropy range is0.43~0.65.Evaluation experiments based on 3329 CT images of original slices of 16-position dinosaur eggshells show that this method has a high efficiency of automatic separation and extraction for a large number of CT images,up to 98.89%.In addition,the calcite of the target can be extracted correctly while retaining more complete target and edge details,and the separation process is accurate and fast.