Xiaoqi Lu

Inner Mongolia University of Science and Technology

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Miaolin Dai

South China Normal University

Chuanfu Zang

South China Normal University

Yiwen Luo

East China Normal University

Zhang Baohua

Nanchang Institute of Technology

Xintong Qiu

South China Normal University

Zhu Ke

Central South University of Forestry and Technology

Yu Gu

Inner Mongolia University of Science and Technology

Xianhua Gan

Guangdong Academy of Forestry

Liang Wu

Shanghai Jiao Tong University

Jia Dongzheng

Inner Mongolia University of Science and Technology

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Inner Mongolia University of Science and Technology

South China Normal University

University of Chinese Academy of Sciences

Inner Mongolia University

Chinese Academy of Sciences

Mongolian University of Science and Technology

Fujian Agriculture and Forestry University

Guangdong Academy of Forestry

Nanjing University of Science and Technology

Southwest University of Science and Technology

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Automatic lung nodule detection using multi-scale dot nodule-enhancement filter and weighted support vector machines in chest computed tomography

PLoS ONE (2019)

Yu Gu Xiaoqi Lu Baohua Zhang Ying Zhao Dahua Yu

A novel CAD scheme for automated lung nodule detection is proposed to assist radiologists with the detection of lung cancer on CT scans. The proposed scheme is composed of four major steps: (1) lung volume segmentation, (2) nodule candidate extraction and grouping, (3) false positives reduction for the non-vessel tree group, and (4) classification for the vessel tree group. Lung segmentation is performed first. Then, 3D labeling technology is used to divide nodule candidates into two groups. For the non-vessel tree group, nodule candidates are classified as true nodules at the false positive reduction stage if the candidates survive the rule-based classifier and are not screened out by the dot filter. For the vessel tree group, nodule candidates are extracted using dot filter. Next, RSFS feature selection is used to select the most discriminating features for classification. Finally, WSVM with an undersampling approach is adopted to discriminate true nodules from vessel bifurcations in vessel tree group. The proposed method was evaluated on 154 thin-slice scans with 204 nodules in the LIDC database. The performance of the proposed CAD scheme yielded a high sensitivity (87.81%) while maintaining a low false rate (1.057 FPs/scan). The experimental results indicate the performance of our method may be better than the existing methods.

Nodule (geology)

Tree (set theory)

10.1371/journal.pone.0210551

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Study and implementation for interactive cutting of 3D medical image based on VTK

Xiaoqi Lu Jia Dongzheng Zhang Baohua Ren Xiaoying

Objectives: Multi-plane cutting, arbitrary plane cutting and box cutting for reconstruction of images are implemented rapidly and quickly by using the Visualization Toolkit (VTK) thus it promotes the application value of medical image data. Several kinds of interactive cutting methods are proposed which aim at meeting different requirements of research and clinical diagnosis, and therefore they provide powerful image support for research education and clinical action. Methods: Firstly the VC++ project is built with VTK and MFC, which are integrated deeply then the 3D volume reconstruction for 2D medical sequence images is realized by using Ray Casting (RC) algorithm. Finally multi-plane cutting, arbitrary plane cutting and box cutting for reconstructed medical images are implemented. Results and Conclusions: The interactive cutting function for 3D medical images is implemented on the basis of Visual Studio 2005 combined with VTK. The interactive cutting can compensate effectively the disadvantages of the medical equipment can only display fixed axial plane information. Meanwhile, it can display spatial information of tissues and organs truly and completely.

Ray casting

10.1109/csqrwc.2011.6037214

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Spatial and temporal dynamics of water resources in typical ecosystems of the Dongjiang River Basin, China

Journal of Hydrology (2022)

Ke Zhu Xintong Qiu Yiwen Luo Miaolin Dai Xiaoqi Lu

10.1016/j.jhydrol.2022.128617

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Citations (23)

SAR Sea Ice Image Segmentation Method based on Low Rank Sparse Representation and Improved MRF Model

Yaogan jishu yu yingyong (2017)