华南理工生物医学信息学实验室

HongminCai, Fei Qi, Junyu Li, Yu Hu, Bin Hu, Yue Zhang, Yiu-ming Cheung*; (2024). Uniform Tensor Clustering by Jointly Exploring Sample Affinities of Various Orders.  IEEE Transactions on Neural Networks and Learning Systems

Xiaoqi Sheng, Hongmin Cai, Yongwei Nie, Shengfeng He, Yiu-Ming Cheung, Jiazhou Chen*; (2024). Modality-Aware Discriminative Fusion Network for Integrated Analysis of Brain Imaging Genomics.  IEEE Transactions on Neural Networks and Learning Systems

Hongmin Cai, Yu Wang, Fei Qi, Zhuoyao Wang, Yiu-ming Cheung*; (2024). Multiview Tensor Spectral Clustering via Co-regularization. IEEE Transactions on Pattern Analysis and Machine Intelligence

Hongmin Cai,  Yu Hu, Fei Qi, Bin Hu, and Yiu-ming Cheung*; (2024). Deep Tensor Spectral Clustering Network via Ensemble of Multiple Affinity Tensors. IEEE Transactions on Pattern Analysis and Machine Intelligence

Hongmin Cai; Weitian Huang; Sirui Yang; Siqi Ding; Yue Zhang; Bin Hu; Fa Zhang; Yiu-Ming Cheung*; (2023). Realize Generative Yet Complete Latent Representation for Incomplete Multi-View Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence

Hongmin Cai, Yi Liao, Lei Zhu, Jiangning Song*; (2023). Improving cancer survival prediction via graph convolutional neural networks learning on protein-protein interaction networks. IEEE Journal of Biomedical and Health Informatics

Hongmin Cai,Xiaoqi Sheng,Guorong Wu,Bin Hu,Yiu-Ming Cheung,Jiazhou Chen*; (2023). Brain Network Classification for Accurate Detection of Alzheimer’s Disease via Manifold Harmonic Discriminant Analysis.  IEEE Transactions on Neural Networks and Learning Systems

Hongmin Cai*, Jinhua Wang, Tingting Dan, Jiao Li, ZhiHao Fan, Weiting Yi, Chunyan Cui, Xinhua Jiang, Li Li*.; (2023). An Online Mammography Database with Biopsy Confirmed Types.  Scientific Data.

Hongmin Cai, Huan Liu, Defu Yang, Guorong Wu, Bin Hu, and Jiazhou Chen*; (2023). Estimating Outlier-Immunized Common Harmonic Waves for Brain Network Analyseson the Stiefel Manifold. IEEE Journal of Biomedical and Health Informatics

Y. Hu, E. Guo, Z. Xie, X. Liu, and H. Cai*; (2023). Robust Multi-view Clustering through Partition Integration on Stiefel Manifold. IEEE Transactions on Knowledge and Data Engineering.

Tingting Dan, Xijie Chen, Miao He, Hongmei Guo, Xiaoqin He, Jiazhou Chen, Jianbo Xian, Yu Hu, Bin Zhang, Nan Wang, Hongning Xie, Hongmin Cai*; (2022). DeepGA for automatically estimating fetal gestational age through ultrasound imaging. Artificial Intelligence In Medicine.

Xiaoqi Sheng, Jiazhou Chen, Hongmin Cai*; (2022).  Deep Manifold Harmonic Network with Dual Attention for Brain Disorder Classification. IEEE Journal of Biomedical and Health Informatics.

Jiazhou Chen, Jie Huang, Yi Liao, Lei Zhu, Hongmin Cai ; (2022). Identify Multiple Gene-Drug Common Modules via Constrained Graph Matching.

Tingting Dan, Hongmin Cai, Zhuobin Huang, Paul Laurienti, Won Hwa Kim & Guorong Wu ; (2022). Neuro-RDM: An Explainable Neural Network Landscape of Reaction-Diffusion Model for Cognitive Task Recognition.

Jiazhou Chen, Guoqiang Han, Aodan Xu, Tatsuya Akutsu, Hongmin Cai ; (2022). Identifying miRNA-gene common and specific regulatory modules for cancer subtyping by a high-order graph matching model.

Jiazhou Chen, Hongmin Cai, Defu Yang, Martin Styner, Guorong Wu ; (2022). Characterizing the propagation pathway of neuropathological events of Alzheimer's disease using harmonic wavelet analysis

Yu Hu, Hongmin Cai  ; (2022). Multi-View Clustering Through Hypergraphs Integration on Stiefel Manifold

Dandan Lu, Ming Li, Yi Liao, Guihua Tao, and Hongmin Cai. ;  (2022). Verifiable Privacy-preserving Queries on Multi-source Dynamic DNA Datasets. IEEE Transactions on Cloud Computing.

Tingting Dan, Zhuobin Huang, Hongmin Cai, Paul J. Laurienti, Guorong Wu. ;  (2022). Learning Brain Dynamics of Evolving Manifold Data Instances Using Geometric-Attention Neural Network. IEEE Transaction on Medical Imaging.

Junyu Li, Jiazhou Chen, Fei Qi, Tingting Dan, Wanlin Weng, Bin Zhang, Haoliang Yuan, Hongmin Cai, Cheng Zhong. ; (2022). Two-Dimensional Unsupervised Feature Selection via Sparse Feature Filter. IEEE Transactions on Cybernetics.

Tao G, Li H, Huang J, Han C, Chen J, Ruan G, Huang W, Hu Y, Dan, T, Zhang B, He S, Cai H. ;  (2022). SeqSeg: A Sequential Method to Achieve Nasopharyngeal Carcinoma Segmentation Free from Background Dominance. Medical Image Analysis.

Yang Li, Tingting Dan, Haojiang Li, Jiazhou Chen, Hong Peng, Lizhi Liu, Hongmin Cai. ; (2022). NPCNet: Jointly Segment Primary Nasopharyngeal Carcinoma Tumors and Metastatic Lymph Nodes in MR Images. IEEE Transactions on Medical Imaging.

H. Peng, H. Wang, B. Zhang, Y. Hu, W. Zhou, & H. Cai. (2021). Multi-dimensional Clustering through Fusion of High-order Similarities. Pattern Recognition.

T. Dan, Y. Hu, C. Han, Z. Fan, Z. Huang, B. Zhang, G. Tao, B. Liu, H. Yu, & H. Cai. (2021). Fusion of multi-source retinal fundus images via automatic registration for clinical diagnosis. Neurocomputing.

Y. Zhang, Q. Huang, S. He, B. Zhang, T. Dan, H. Peng, & H. Cai. (2021). Deep Multi-view Clustering via Iteratively Self-supervised Universal and Specific Space Learning. IEEE transactions on cybernetics.

D. Lu, Y. Zhang, L. Zhang, H. Wang, W. Weng, L. Li, &  H. Cai. (2021). Methods of privacy-preserving genomic sequencing data alignments. Briefings in Bioinformatics, 2021.

H. Wang, G. Han, J. Li, B. Zhang, J. Chen, Y. Hu, C. Han, & H. Cai. (2021). Learning Task-driving Affinity Matrix for Accurate Multi-view Clustering through Tensor Subspace Learning. Information Sciences.

Peng, H., Chen, J., Wang, H., Hu, Y., & Cai H. (2020). Integrating Tensor Similarity to Enhance Clustering Performance. IEEE Transactions on Pattern Analysis and Machine Intelligence.

Rong, W., Zhuo, E., Peng, H., Chen, J., Wang, H., Han, C., & Cai H. (2020). Learning a consensus affinity matrix for multi-view clustering via subspaces merging on Grassmann manifold. Information Sciences.

Wang, H., Han, G., Zhang, B., Tao, G., & Cai H. (2020). Exsavi: Excavating Both Sample-wise and View-wise Relationships to Boost Multi-view Subspace Clustering. Neurocomputing.

Wang, H., Han, G., Li, H., Tao, G., Zhuo, E., Liu, L., Cai H. & Ou, Y. (2020). A Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multimodalities MR Sequences. Computational and Mathematical Methods in Medicine, 2020.

Yang, J., Dong, X., Hu, Y., Peng, Q., Tao, G., Ou, Y., Cai H.. & Yang, X. (2020). Fully Automatic Arteriovenous Segmentation in Retinal Images via Topology-Aware Generative Adversarial Networks. Interdisciplinary Sciences: Computational Life Sciences, 1-12.

Huang, Q., Zhang, Y., Peng, H., Dan, T., Weng, W., & Cai H. (2020). Deep subspace clustering to achieve jointly latent feature extraction and discriminative learning. Neurocomputing, 404, 340-350.

Chen, X., He, M., Dan, T., Wang, N., Lin, M., Zhang, L., Cai H. & Xie, H. (2020). Automatic Measurements of Fetal Lateral Ventricles in 2D Ultrasound Images Using Deep Learning. Frontiers in neurology, 11, 526.

Wei, Z., Zhang, Y., Weng, W., Chen, J., & Cai H. (2020). Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets. Briefings in Bioinformatics.

Huang, J., Chen, J., Zhang, B., Zhu, L., & Cai H (2020). Evaluation of gene–drug common module identification methods using pharmacogenomics data. Briefings in Bioinformatics.

Xie, B., Lei, T., Wang, N., Cai H., Xian, J., He, M., ... & Xie, H. (2020). Computer-aided diagnosis for fetal brain ultrasound images using deep convolutional neural networks. International Journal of Computer Assisted Radiology and Surgery.

Xie, H., Wang, N., He, M., Zhang, L.,Cai H., Xian, J., ... & Yang, Y. (2020). Using deep learning algorithms to classify fetal brain ultrasound images as normal or abnormal. Ultrasound in Obstetrics & Gynecology.

Chen, J., Han, G., Cai H., Yang, D., Laurienti, P. J., Styner, M., ... & ADNI, A. S. D. N. I. (2020). Learning Common Harmonic Waves on Stiefel Manifold--A New Mathematical Approach for Brain Network Analyses. arXiv preprint arXiv:2007.13533.

Zeng, J., Cai H., Peng, H., Wang, H., Zhang, Y., & Akutsu, T. (2020). Causalcall: Nanopore basecalling using a temporal convolutional network. Frontiers in Genetics, 10, 1332.

Zeng, J.,Cai H., & Akutsu, T. (2020, January). Breast Cancer Subtype by Imbalanced Omics Data through A Deep Learning Fusion Model. In Proceedings of the 2020 10th International Conference on Bioscience, Biochemistry and Bioinformatics (pp. 78-83).

Weng, W., Zhou, W., Chen, J., Peng, H., &Cai, H. (2020). Enhancing multi-view clustering through common subspace integration by considering both global similarities and local structures. Neurocomputing, 378, 375-386.

Li, Z., Zhang, Z., Qin, J., Li, S., & Cai, H. (2019). Low-rank analysis–synthesis dictionary learning with adaptively ordinal locality. Neural Networks, 119, 93-112.

Huang, J. B., Zhuo, E., Li, H., Liu, L., Cai, H.., & Ou, Y. (2019, October). Achieving Accurate Segmentation of Nasopharyngeal Carcinoma in MR Images Through Recurrent Attention. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 494-502). Springer, Cham.

Chen, J., Han, G., Xu, A., & Cai, H. (2019). Identification of Multidimensional Regulatory Modules through Multi-graph Matching with Network Constraints.IEEE Transactions on Biomedical Engineering.

Peng, H., Chen, J., Wang, H., Hu, Y., & Cai, H. (2019). Integrating Tensor Similarity to Enhance Clustering Performance.arXiv preprint arXiv:1905.03920.

Zhuo, E. H., Zhang, W. J., Li, H. J., Zhang, G. Y., Jing, B. Z., Zhou, J., ... &Cai, H. M. (2019). Radiomics on multi-modalities MR sequences can subtype patients with non-metastatic nasopharyngeal carcinoma (NPC) into distinct survival subgroups.European radiology, 1-10.

Cai, H., Pang, X., Dong, D., Ma, Y., Huang, Y., Fan, X., ... & Liu, S. (2019). Molecular Decision Tree Algorithms Predict Individual Recurrence Pattern for Locally Advanced Nasopharyngeal Carcinoma.Journal of Cancer,10(15), 3323.

Xu, A., Chen, J., Peng, H., Han, G., & Cai, H. (2019). Simultaneous interrogation of cancer omics to identify subtypes with significant clinical differences.Frontiers in genetics,10, 236.

Cai, H., Huang, Q., Rong, W., Song, Y., Li, J., Wang, J., ... & Li, L. (2019). Breast Microcalcification Diagnosis Using Deep Convolutional Neural Network from Digital Mammograms.Computational and mathematical methods in medicine,2019.

You, Y., Cai, H., & Chen, J. (2018). Low rank representation and its application in bioinformatics.Current Bioinformatics,13(5), 508-517.

Yang, X., Han, G., Chen, J., & Cai, H. (2018). Finding Correlated Patterns via High-Order Matching for Multiple Sourced Biological Data.IEEE Transactions on Biomedical Engineering,66(4), 1017-1025.

Jiang, X., Xie, F., Liu, L., Peng, Y., Cai, H., & Li, L. (2018). Discrimination of malignant and benign breast masses using automatic segmentation and features extracted from dynamic contrast-enhanced and diffusion-weighted MRI.Oncology letters,16(2), 1521-1528.

Chen, J., Peng, H., Han, G., Cai, H., & Cai, J. (2018). HOGMMNC: a higher order graph matching with multiple network constraints model for gene–drug regulatory modules identification.Bioinformatics,35(4), 602-610.

Cai, J., Cai, H., Chen, J., & Yang, X. (2018). Identifying “Many-to-Many” Relationships Between Gene-Expression Data and Drug-Response Data Via Sparse Binary Matching.IEEE/ACM transactions on computational biology and bioinformatics.

Huang, J., Zhou, Y., Xu, A., Zhuo, E., Jin, X., & Cai, H. (2017, November). A copy-number variation detection pipeline for single cell sequencing data on BGI online. In2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)(pp. 243-246). IEEE.

Cai, H., Chen, P., Chen, J., Cai, J., Song, Y., & Han, G. (2017). Wavedec: a wavelet approach to identify both shared and individual patterns of copy-number variations.IEEE Transactions on Biomedical Engineering,65(2), 353-364.

Wei, Z., Shu, C., Zhang, C., Huang, J., & Cai, H. (2017). A short review of variants calling for single-cell-sequencing data with applications.The international journal of biochemistry & cell biology,92, 218-226.

Yang, X., Han, G., Cai, H., & Song, Y. (2017). Recovering hidden diagonal structures via non-negative matrix factorization with multiple constraints.IEEE/ACM transactions on computational biology and bioinformatics.

Jiang, X., Xie, F., Liu, L. Z., Peng, Y. X., Cai, H. M., & Li, L. (2017, March). Discrimination of malignant and benign breast masses using automatic segmentation and region of interest-based features extracted from MRI. European Congress of Radiology 2017.

Zhang, C., Cai, H., Huang, J., & Xu, B. (2016, December). Multi-norm constrained optimization methods for calling copy number variants in single cell sequencing data. In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 156-160). IEEE.

Xu, B., Cai, H., Zhang, C., Yang, X., & Han, G. (2016). Copy number variants calling for single cell sequencing data by multi-constrained optimization. Computational biology and chemistry, 63, 15-20.

Zhang, C., Cai, H., Huang, J., & Xu, B. (2016, December). Multi-norm constrained optimization methods for calling copy number variants in single cell sequencing data. In2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)(pp. 156-160). IEEE.

Zhang, C., Cai, H., Huang, J., & Song, Y. (2016). nbCNV: a multi-constrained optimization model for discovering copy number variants in single-cell sequencing data.BMC bioinformatics,17(1), 384.

Huang, W., Cai, H., Shao, W., Xu, B., & Li, F. (2016, August). MDAGenera: An Efficient and Accurate Simulator for Multiple Displacement Amplification. InInternational Conference on Intelligent Computing(pp. 258-267). Springer, Cham.

Xu, B., Cai, H., Zhang, C., Yang, X., & Han, G. (2016). Copy number variants calling for single cell sequencing data by multi-constrained optimization.Computational biology and chemistry,63, 15-20.

Wang, J., Yang, X., Cai, H., Tan, W., Jin, C., & Li, L. (2016). Discrimination of breast cancer with microcalcifications on mammography by deep learning.Scientific reports,6, 27327.

Jiang, R., You, R., Pei, X. Q., Zou, X., Zhang, M. X., Wang, T. M., ... & Hua, Y. J. (2016). Development of a ten-signature classifier using a support vector machine integrated approach to subdivide the M1 stage into M1a and M1b stages of nasopharyngeal carcinoma with synchronous metastases to better predict patients' survival.Oncotarget,7(3), 3645.

Cheng, X., Cai, H., Zhang, Y., Xu, B., & Su, W. (2015). Optimal combination of feature selection and classification via local hyperplane based learning strategy.BMC bioinformatics,16(1), 219.

Li, T., Zhang, C., Xu, B., Cai, H., & Li, F. (2015, November). MALBACsim: A Multiple Annealing and Looping Based Amplification Cycles simulator. In2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)(pp. 97-100). IEEE.

Chen, P., Huang, W., Shao, W., & Cai, H. (2015, September). Discrimination of recurrent CNVs from individual ones from multisample aCGH by jointly constrained minimization. InProceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics(pp. 186-193). ACM.

Cai, H., Liu, L., Peng, Y., Wu, Y., & Li, L. (2014). Diagnostic assessment by dynamic contrast-enhanced and diffusion-weighted magnetic resonance in differentiation of breast lesions under different imaging protocols.BMC cancer,14(1), 366.

Cai, H., Ruan, P., Ng, M., & Akutsu, T. (2014). Feature weight estimation for gene selection: a local hyperlinear learning approach.BMC bioinformatics,15(1), 70.

TIAN, H., Cai, H., & LAI, J. (2014). An Adaptive PM Model Based on Difference Eigenvalue for Image Restoration.Chinese Journal of Electronics,23(4).

Tian, H., Cai, H., & Lai, J. (2014). A novel diffusion system for impulse noise removal based on a robust diffusion tensor.Neurocomputing,133, 222-230.

Xu, B., Li, T., Luo, Y., Xu, R., & Cai, H. (2014, May). An Empirical Algorithm for Bias Correction Based on GC Estimation for Single Cell Sequencing. InPacific-Asia Conference on Knowledge Discovery and Data Mining(pp. 15-21). Springer, Cham.

Cai, H., Peng, Y., Ou, C., Chen, M., & Li, L. (2014). Diagnosis of breast masses from dynamic contrast-enhanced and diffusion-weighted MR: a machine learning approach.PloS one,9(1), e87387.

Cai, H., Yang, Z., Cao, X., Xia, W., & Xu, X. (2014). A new iterative triclass thresholding technique in image segmentation.IEEE transactions on image processing,23(3), 1038-1046.

Su, W., Wu, H., Li, Y., Zhao, J., Lochovsky, F. H., Cai, H., & Huang, T. (2013). Understanding query interfaces by statistical parsing.ACM Transactions on the Web (TWEB),7(2), 8.

Cheng, X., Cai, H., He, P., Zhang, Y., & Tian, R. (2013). Combination of effective machine learning techniques and chemometric analysis for evaluation of Bupleuri Radix through high-performance thin-layer chromatography.Analytical Methods,5(22), 6325-6330.

Cai, H., & Ng, M. (2012, November). Optimal Combination of Feature Weight Learning and Classification Based on Local Approximation. InInternational Conference on Data and Knowledge Engineering(pp. 86-94). Springer, Berlin, Heidelberg.

Cai, H., & Ng, M. (2012, May). Feature weighting by RELIEF based on local hyperplane approximation. InPacific-Asia Conference on Knowledge Discovery and Data Mining(pp. 335-346). Springer, Berlin, Heidelberg.

Fan, X. J., Wan, X. B., Huang, Y., Cai, H. M., Fu, X. H., Yang, Z. L., ... & Wang, L. (2012). Epithelial–mesenchymal transition biomarkers and support vector machine guided model in preoperatively predicting regional lymph node metastasis for rectal cancer.British journal of cancer,106(11), 1735.

Wan, X. B., Zhao, Y., Fan, X. J., Cai, H. M., Zhang, Y., Chen, M. Y., ... & Hong, M. H. (2012). Molecular prognostic prediction for locally advanced nasopharyngeal carcinoma by support vector machine integrated approach.PloS one,7(3), e31989.

Cai, H., Cui, C., Tian, H., Zhang, M., & Li, L. (2012). A novel approach to segment and classify regional lymph nodes on computed tomography images.Computational and mathematical methods in medicine,2012.

Cui, C., Cai, H., Liu, L., Li, L., Tian, H., & Li, L. (2011). Quantitative analysis and prediction of regional lymph node status in rectal cancer based on computed tomography imaging.European radiology,21(11), 2318-2325.

Tian, H., Cai, H., Lai, J. H., & Xu, X. (2011, September). Effective image noise removal based on difference eigenvalue. In2011 18th IEEE International Conference on Image Processing(pp. 3357-3360). IEEE.

Cai, H. (2011, August). Improvements over adaptive local hyperplane to achieve better classification. InIndustrial Conference on Data Mining(pp. 1-10). Springer, Berlin, Heidelberg.

Tian, H., Cai, H., Cui, C., & Li, L. (2011, June). Quality enhancement with adaptive edge preservation for lymph nodal images. InAIP Conference Proceedings(Vol. 1371, No. 1, pp. 341-342). AIP.

Tian, H. Y., Cai, H. M., Xu, X., & Lai, J. H. (2011). Improved partial differential equation-based method to remove noise in image enhancement.

Cui, C., Cai, H., Liu, L., Li, L., Tian, H., & Li, L. (2011). Quantitative analysis and prediction of regional lymph node status in rectal cancer based on computed tomography imaging.European radiology,21(11), 2318-2325.

Tian H,Cai, H., Lai J H, et al. Effective image noise removal based on difference eigenvalue[C]//2011 18th IEEE International Conference on Image Processing. IEEE, 2011: 3357-3360.

Cai, H., Xu, X., Lu, J., Lichtman, J., Yung, S. P., & Wong, S. T. (2008). Using nonlinear diffusion and mean shift to detect and connect cross-sections of axons in 3D optical microscopy images.Medical Image Analysis,12(6), 666-675.

Verma, R., Zacharaki, E. I., Ou, Y., Cai, H., Chawla, S., Lee, S. K., ... & Davatzikos, C. (2008). Multiparametric tissue characterization of brain neoplasms and their recurrence using pattern classification of MR images.Academic radiology,15(8), 966-977.

Cai, H., Verma, R., Ou, Y., Lee, S. K., Melhem, E. R., & Davatzikos, C. (2007, April). Probabilistic segmentation of brain tumors based on multi-modality magnetic resonance images. In2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro(pp. 600-603). IEEE.

Ou, Y., Cai, H., Lee, S. K., Melhem, E. R., Davatzikos, C., & Verma, R. (2007). Cascaded segmentation of brain tumors using multi-modality MR profiles.

Zhang, Y., Xu, X., Cai, H., Yung, S. P., & Wong, S. T. (2007). A new nonlinear diffusion method to improve image quality. In2007 IEEE International Conference on Image Processing(Vol. 1, pp. I-329). IEEE.

Cai, H., Xu X, Lu J, et al. Use mean shift to track neuronal axons in 3D[C]//2006 IEEE/NLM Life Science Systems and Applications Workshop. IEEE, 2006: 1-2.

Cai, H., Xu, X., Lu, J., Lichtman, J. W., Yung, S. P., & Wong, S. T. (2006). Repulsive force based snake model to segment and track neuronal axons in 3D microscopy image stacks.NeuroImage,32(4), 1608-1620.

Cai, H., Xu, X., Lu, J., Lichtman, J., Yung, S. P., & Wong, S. T. (2006, April). Shape-constrained repulsive snake method to segment and track neurons in 3D microscopy images. In3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006.(pp. 538-541). IEEE.

Cai, H., Xu, X., Lu, J., Lichtman, J., Yung, S. P., & Wong, S. T. C. (2005, December). Segment and track neurons in 3D by repulsive snake method. In2005 International Symposium on Intelligent Signal Processing and Communication Systems(pp. 529-532). IEEE.

Cheng J, Xu X, Cai, H., et al. New snake algorithm to track neuronal structure in microscopy image[C]//2005 International Symposium on Intelligent Signal Processing and Communication Systems. IEEE, 2005: 537-540.