Abstract
INTRODUCTION: The gray resolution of anatomical image of cervical nerve root syndrome is low, that can not be mined accurately. OBJECTIVES: Aiming at the defect of low gray resolution of anatomical images, an image mining method using visual perception technology was studied. METHODS: According to the visual perception technology, the internal parameter matrix and external parameter matrix of binocular visual camera were determined by coordinate transformation, and the anatomical images of cervical nerve root syndrome were collected. The collected images are smoothed and enhanced by nonlinear smoothing algorithm and multi-scale nonlinear contrast enhancement method. The directional binary simple descriptor method is selected to extract the features of the enhanced image; Using K-means clustering algorithm, the anatomical image mining of cervical nerve root syndrome is completed by obtaining the initial clustering center and image mining. RESULTS: Experimental results show that the information entropy of the images mined by the proposed method is higher than 5, the average gradient is greater than 7, the edge information retention is greater than 0.7, the peak signal-to-noise ratio is higher than 30 dB, and the similarity of the same category of images is greater than 0.9. CONCLUSIONS: This method can effectively mine the anatomical images of cervical nerve root syndrome and provide an important basis for the diagnosis and treatment of cervical nerve root syndrome.
Publisher
European Alliance for Innovation n.o.
Subject
Health Informatics,Computer Science (miscellaneous)
Reference20 articles.
1. Fan, W. , Wang, R. & Bouguila, N. (2021). Simultaneous positive sequential vectors modeling and unsupervised feature selection via continuous hidden markov models. Pattern Recognition, 119(2), 108073.
2. Antol, M. , Ha, J. , Slanináková, T. & Dohnal, V. (2021). Learned metric index — proposition of learned indexing for unstructured data. Information Systems, 100(8), 101774.
3. Liu, S. , Xu, X. , Zhang, Y. , Khan, M. & Fu, W. (2022) A Reliable Sample Selection Strategy for Weakly-supervised Visual Tracking, IEEE Transactions on Reliability, online first, 10.1109/TR.2022.3162346
4. Zhang, L. (2020). Feature mining simulation of video image information in multimedia learning environment based on bow algorithm. The Journal of Supercomputing, 76(1), 113-121.
5. Haq, N. F. , Moradi, M. & Wang, Z. J. (2020). A deep community based approach for large scale content based x-ray image retrieval. Medical Image Analysis, 68(2), 101847.