Zobrazeno 81 - 90
of 127
pro vyhledávání: '"Jiayin Zhou"'
Autor:
Yi Su, Jiang Liu, Chee-Kong Chui, Jiayin Zhou, Tao Yang, Stephen Chang, Jing Bing Zhang, Weimin Huang
Publikováno v:
2011 6th IEEE Conference on Industrial Electronics and Applications.
One challenge in laparoscopic cholecystectomy surgery simulation is to construct a fast and accurate deformable gallbladder model. This paper proposed an improved multi-layer mass-spring modeling method which can adapt well to the built-in accelerati
Publikováno v:
Medical Imaging: Computer-Aided Diagnosis
Computer aided liver tumor detection and diagnosis can assist radiologists to interpret abnormal features in liver CT scans. In this paper, a general frame work is proposed to automatically detect liver focal mass lesions, conduct differential diagno
Autor:
Wee Kheng Leow, Qi Tian, Zhimin Wang, Sudhakar K. Venkatesh, Feng Ding, Weimin Huang, Jiayin Zhou, Wei Xiong
Publikováno v:
Medical Imaging: Image Processing
Robust and efficient segmentation tools are important for the quantification of 3D liver and liver tumor volumes which can greatly help clinicians in clinical decision-making and treatment planning. A two-module image analysis procedure which integra
Autor:
Chee-Kong Chui, Stephen Chang, Jiang Liu, Tao Yang, Weimin Huang, Jing Zhang, Gim Han Law, Yi Su, Jiayin Zhou
Publikováno v:
Medical Imaging: Image-Guided Procedures
This paper presents an approach to gallbladder shape comparison by using 3D shape modeling and decomposition. The gallbladder models can be used for shape anomaly analysis and model comparison and selection in image guided robotic surgical training,
Publikováno v:
Machine Learning in Medical Imaging ISBN: 9783642243189
MLMI
MLMI
To achieve robust classification performance of support vector machine (SVM), it is essential to have balanced and representative samples for both positive and negative classes. A novel three-stage hybrid SVM (HSVM) is proposed and applied for the se
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f39b945642b55e7d087af2f819189ae4
https://doi.org/10.1007/978-3-642-24319-6_17
https://doi.org/10.1007/978-3-642-24319-6_17
Publikováno v:
2010 3rd International Conference on Biomedical Engineering and Informatics.
A semi-automatic method was developed for the segmentation of 3D gallbladders (GB) from CT images, in order to construct a patient-specific model for a surgical training system. First a support vector machine (SVM) classifier was trained to extract G
Autor:
Chee-Kong Chui, L. Xiong, Weimin Huang, Jiayin Zhou, Jing Zhang, Yi Su, C.L. Teo, Stephen Chang, Tao Yang, Liangjing Yang, Jiang Liu
Publikováno v:
2010 3rd International Conference on Biomedical Engineering and Informatics.
Modeling forces applied to cut biological material with laparoscopic scissors is important for haptic rendering in laparoscopic surgical simulation. The cutting process is characterized in deformation and fracture. An analytical model for cutting hum
Autor:
Chee-Kong Chui, Jing Qin, Stephen Chang, Tao Yang, Yi Su, Jing Zhang, Weimin Huang, Jiayin Zhou, Beng Hai Lee, Jiang Liu
Publikováno v:
2010 3rd International Conference on Biomedical Engineering and Informatics.
A challenge in virtual reality based laparoscopic cholecystectomy simulation is to construct a fast and accurate deformable gallbladder model. This paper proposes a multi-layer mass-spring model which can adapt well to the built-in accelerating algor
Publikováno v:
ICIP
The construction of probabilistic liver atlases has received little attention in the past. Existing methods are based on landmarks and are sensitive to their choices and placements. We propose an iterative landmark-free method based on dense volumes
Publikováno v:
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 11(Pt 1)
In this paper, we consider the extraction of nasopharyngeal carcinoma lesion from MR images as a region segmentation problem. We propose a semi-supervised segmentation approach to segment the lesion in two steps. First, a metric is learned in a super