Extraction of Liver Capsule from High-Frequency Ultrasound Images via Drift Iterative Search Algorithm
- 1 School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai, China
- 2 School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai, China
- 3 School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai, China
- 4 Department of Ultrasound, Changzheng Hospital Affiliated to Second Military Medical University, Shanghai, China
- 5 Zhejiang Institute of Standardization, Hangzhou, China
- 6 School of Computer and Computing Science, Hangzhou City University, Hangzhou, China
Abstract
Aiming at the prior medical knowledge that hepatic ascites only occurs in the severe period of liver cirrhosis, and the severe rupture of the liver capsule curve, when ascites occurs visually, can easily lead to the wrong location of the liver capsule, a transposed grayscale statistical threshold method is proposed to solve the problem. Realize the identification of liver ascites. By analyzing the visual characteristics of the liver image, the gray value of the upper half of the ultrasound image is counted column by column from a mathematical point of view, the gray distribution curve is drawn, and the relevant threshold is set for corresponding judgment. At the same time, the gray value above the ascites detection boundary is set to zero. The ablation experiment proved that the ascites detection method and post-processing operation proposed in this paper provide effective support for the precise positioning of the liver capsule curve, quantitative analysis and diagnosis of liver cirrhosis in the later stage. The Hessian matrix is sensitive to linear structure to achieve image enhancement. In view of the low accuracy of the existing liver envelope curve detection method and the incomplete quantitative evaluation of liver cirrhosis, it is proposed to use drift iteration under the synergistic effect of multiple filters. A search algorithm extracts the liver capsule.
- Liu, X., Zhan, Z.Q., Yan, M., et al. (2017) Computer-Aided Cirrhosis Diagnosis via Automatic Liver Capsule Extraction and Combined Geometry-Texture Features. 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, 10-14 July 2017, 865-870.
- Wang, S.H., Xiang, L., Zhao, J.W., et al. (2016) Learning to Diagnose Cirrhosis via Combined Liver Capsule and Parenchyma Ultrasound Image Features. 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, 15-18 December 2016, 799-804.
- Zhao, J.W., Wang, S.H., Liu, X., Liu, Y. and Chen, Y.Q. (2018) Early Diagnosis of Cirrhosis via Automatic Location and Geometric Description of Liver Capsule. The Visual Computer, 34, 1677-1689. https://doi.org/10.1007/s00371-017-1441-2
- Liu, X., Ma, R.L., Zhao, J.W., et al. (2021) A Clinical Decision Support System for Predicting Cirrhosis Stages via High Frequency Ultrasound Images. Expert Systems with Applications, 175, Article ID: 114680. https://doi.org/10.1016/j.eswa.2021.114680
- Wang, W.B., Li, C.B. and Zheng, C.J. (2020) Hessian-Based Directional Adaptive Gabor Wavelet for Retinal Vessel Segmentation. Advances in Lasers and Optoelectronics, 57, 208-215.
- Pugh, R. (2010) Transection of the Oesophagus for Bleeding Oesophageal Varices. British Journal of Surgery, 60, 646-649. https://doi.org/10.1002/bjs.1800600817
- Frangi, R.F., Niessen, W.J., Vincken, K.L. and Viergever, M.A. (1998) Multiscale Vessel Enhancement Filtering. In: Wells, W.M., Colchester, A. and Delp, S., Eds, International Conference on Medical Image Computing and Computer-Assisted Intervention, Vol. 1496, Springer, Berlin, 130-137. https://doi.org/10.1007/BFb0056195
- Virmani, J., Kumar, V., Kalra, N. and Khandelwal, N. (2013) Prediction of Liver Cirrhosis Based on Multiresolution Texture Descriptors from B-Mode Ultrasound. International Journal of Convergence Computing, 1, 19-37. https://doi.org/10.1504/IJCONVC.2013.054658
- Meyes, R., Lu, M., de Puiseau, C.W., et al. (2019) Ablation Studies in Artificial Neural Networks. ArXiv: 1901.08644.
- Shortliffe, E.H. and Buchanan, B.G. (1975) A Model of Inexact Reasoning in Medicine. Mathematical Biosciences, 23, 351-379. https://doi.org/10.1016/0025-5564(75)90047-4
- Wang, S.H., Liu, X., Zhao, J., Song, J.L., Zhang, J.Q. and Chen, Y.Q. (2016) Learning to Diagnose Cirrhosis via Combined Liver Capsule and Parenchyma Ultrasound Image Features. 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, 15-18 December 2016, 799-804. https://doi.org/10.1109/BIBM.2016.7822627