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Beiji Zou
Researcher at Central South University
Publications - 143
Citations - 1698
Beiji Zou is an academic researcher from Central South University. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 18, co-authored 104 publications receiving 1083 citations. Previous affiliations of Beiji Zou include China Mobile & Chinese Ministry of Education.
Papers
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Journal ArticleDOI
Retinal vessel segmentation in colour fundus images using Extreme Learning Machine
Chengzhang Zhu,Beiji Zou,Beiji Zou,Rongchang Zhao,Rongchang Zhao,Cui Jinkai,Cui Jinkai,Xuanchu Duan,Zailiang Chen,Zailiang Chen,Yixiong Liang,Yixiong Liang +11 more
TL;DR: A supervised method based on Extreme Learning Machine (ELM) is proposed to segment retinal vessel, which has potential applications for real-time computer-aided diagnosis and disease screening and on a new Retinal Images for Screening (RIS) database.
Proceedings ArticleDOI
Effective prediction of three common diseases by combining SMOTE with Tomek links technique for imbalanced medical data
TL;DR: A powerful preprocessing method by combining Synthetic Minority Oversampling Technique (SMOTE) with Tomek links technique and then is applied to the imbalanced medical data sets of the three diseases, showing results that are much superior compared with that of using only SMOTE.
Journal ArticleDOI
Selective color transfer with multi-source images
Yao Xiang,Beiji Zou,Hong Li +2 more
TL;DR: An improved EM method is presented to model regional color distribution of the target image by Gaussian Mixture Model, then appropriate reference colors are automatically selected from the given source images to color each target region.
Journal ArticleDOI
A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images.
Qing Liu,Qing Liu,Qing Liu,Beiji Zou,Beiji Zou,Jie Chen,Wei Ke,Kejuan Yue,Zailiang Chen,Zailiang Chen,Guoying Zhao +10 more
TL;DR: The experimental results show that the proposed location-to-segmentation strategy achieves 76% in sensitivity and 75% in positive prediction value (PPV), which both outperform the state of the art methods significantly.
Journal ArticleDOI
A novel robust reversible watermarking scheme for protecting authenticity and integrity of medical images
TL;DR: Experimental results demonstrate that the proposed lossless scheme not only has remarkable imperceptibility and sufficient robustness but also provides reliable authentication, tamper detection, localization, and recovery functions, which outperforms existing schemes for protecting medical images.