A
skull mark point recognition method based on bone seam boundary extraction and intersection recognition comprises the steps that firstly, a segmented
skull three-dimensional CT tag image is obtained to serve as input; then, performing binarization
processing and morphological expansion operation on each skeleton
label, and extracting boundary voxels of each skeleton; thirdly, fusing and superposing all skeleton boundaries, and detecting
voxel points which are overlapped at two or more skeleton boundaries as candidate bone seam intersections; then, classifying and identifying the candidate intersection points based on a predefined skeleton combination rule, and determining the types of the
skull anatomical mark points corresponding to the candidate intersection points; and finally, a geometric feature optimization strategy is adopted for the position of each mark point, and the final space coordinate of each mark point is obtained through calculation. The invention further provides a skull mark point
recognition system. The whole process does not depend on
machine learning model training, the result
interpretability is high, a large amount of
labeled data is not needed, the positioning precision is high, and the method can be widely applied to scenes such as medical image three-dimensional reconstruction and
surgical planning navigation.