This paper presents the development of a low-cost cataract surgery simulator for trainees to practise phacoemulsification procedures with computer-generated models in virtual environments. It focuses on the training of cornea incision, capsulorrhexis and phaco-sculpting, which are simulated interactively with computationally efficient algorithms developed… (More)
Physically based models and simulation are usually computationally intensive and not suitable for real-time interactive virtual reality applications including on-line medical training and surgical simulation. In this paper, we propose and develop a web-based scalable deformable model by simulating deformation of soft tissues as a successive force… (More)
An effective deformable model based on a successive force propagation process is proposed. It avoids the laborious stiffness matrix formulation and is scalable simply by controlling the penetration depth. Mechanical tests are performed to evaluate its feasibility for modeling real tissues. An interactive system is developed using a commercial haptic device.
Clinical data are dynamic in nature, often arranged hierarchically and stored as free text and numbers. Effective management of clinical data and the transformation of the data into structured format for data analysis are therefore challenging issues in electronic health records development. Despite the popularity of relational databases, the scalability of… (More)
A key challenge of deformable simulation is to satisfy the conflicting requirements of real-time interactivity and physical realism. In this paper, we present the mass–spring-based force propagation model (FPM) in which the simulation speed is tunable to maintain a balance between the two criteria. Deformation is modeled as a result of force propagation… (More)
Inductive transfer learning has attracted increasing attention for the training of effective model in the target domain by leveraging the information in the source domain. However, most transfer learning methods are developed for a specific model, such as the commonly used support vector machine, which makes the methods applicable only to the adopted… (More)
Classical fuzzy system modeling methods consider only the current scene where the training data are assumed to be fully collectable. However, if the data available from the current scene are insufficient, the fuzzy systems trained by using the incomplete datasets will suffer from weak generalization capability for the prediction in the scene. In order to… (More)