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

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 similar to 188, and the information entropy range is 0. 43 similar to 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.