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

Chun-yu CHEN; Ying-cong HUANG; Qiang WANG; Guang-rong LI; Yue-shun HE

doi:10.37188/CJLCD.2021-0343

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

Image Processing | 更新时间：2022-07-25

- Experimental research on automatic object extraction from CT image based on information entropy—taking CT image of dinosaur eggshell slices as an example—taking CT image of dinosaur eggshell slices as an example
- Chinese Journal of Liquid Crystals and Displays Vol. 37, Issue 7, Pages: 891-899(2022)
- 作者机构：
  
  1.东华理工大学地球科学学院，江西南昌 330013
  2.东华理工大学网络与信息中心，江西南昌 330013
  3.东华理工大学信息工程学院，江西南昌 330013
  4.江西省数字国土重点实验室，江西南昌 330013
  5.江西省放射性地学大数据技术工程实验室，江西南昌 330013
  6.核资源与环境国家重点实验室，江西南昌 330013
  7.中国科学院古脊椎动物与古人类研究所，北京 100044
- 作者简介：
- 基金信息：
  
  the National Natural Science Foundation of China(41672012;41688103;41202160);The Strategic Priority Research Program of the Chinese Academy of Sciences （TYPE B）(XDB26000000);Open Foundation of State Key Laboratory of Nuclear Resources and Environment(NRE1913);Open Foundation of Jiangxi Provincial Radiology Big Data Technology Engineering Laboratory(JELRGBDT202002);Open Foundation of Jiangxi Provincial Key Laboratory of Digital L(DLLJ201916);Doctoral Research Foundation of East China Institute of Technology(DHBK2018021)
- DOI：10.37188/CJLCD.2021-0343
  CLC： TP391.41;TP183
- Received：27 December 2021，
  
  Revised：08 February 2022，
  
  Published：05 July 2022
- 稿件说明：
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Chun-yu CHEN, Ying-cong HUANG, Qiang WANG, et al. Experimental research on automatic object extraction from CT image based on information entropy—taking CT image of dinosaur eggshell slices as an example—taking CT image of dinosaur eggshell slices as an example[J]. Chinese journal of liquid crystals and displays, 2022, 37(7): 891-899.
DOI：

Chun-yu CHEN, Ying-cong HUANG, Qiang WANG, et al. Experimental research on automatic object extraction from CT image based on information entropy—taking CT image of dinosaur eggshell slices as an example—taking CT image of dinosaur eggshell slices as an example[J]. Chinese journal of liquid crystals and displays, 2022, 37(7): 891-899. DOI： 10.37188/CJLCD.2021-0343.

摘要

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3 329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。

Abstract

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 is 0.43~0.65.Evaluation experiments based on 3 329 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.

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