Mass Spring Model (MSM) is often used to model human liver due to its easy implementation and low computational complexity. This paper focuses on development of a real-time human liver simulation, which enabled an efficient implementation of liver mechanical response incorporating nonlinearity and viscoelasticity properties. Optimization of the computational problems is necessary to permit real-time liver simulation. Adam Variable Step-Size Predictor-Corrector (AVSPC) method is preferred to solve the governing differential equations and considerably more accurate than Fourth-order Runge-Kutta (RK4) method. AVSPC method with higher accuracy leads to higher quality of liver simulation. Reduction of local truncation error is needed to maintain accuracy as well as preventing model state from rapid change. Optimized time step size 0.0063 was implemented in CHAI 3D to simulate real-time liver deformation caused by surgical indenter. Deformation of liver with higher deformation rate appears to have higher stiffness and higher stress relaxation rate. In conclusion, this model is a plausibly significant liver tissue model which is more suitable for real-time interaction with lower computational cost, more accurate, realistic and acceptable to be used in the near future.

One of the fundamental components of a surgical simulator is a deformable object. Two main approaches used in surgical simulation to model deformable objects are Finite Element Model (FEM) and Mass Spring Model (MSM). MSM is often preferred due to its simplicity and low computational cost. However, setting of appropriate model parameters such as mass, spring stiffness and damping coefficients in order to reproduce mechanical responses remains an issue. In this paper, biomechanical parameters (Poisson’s values, density) are integrated into MSM based on a tetrahedral structured network in modeling of liver with and without tumor. For the identification of parameters in a real time surgical simulation, Barycentric mass lumping, Lloyd’s approach, Rayleigh formula and Fourth order Runge-Kutta integration method are used to determine the node mass, spring stiffness, damping coefficient and suitable time step respectively. The resulted node mass, spring stiffness and damping coefficient for liver without tumor and with tumor are 1.9825kg, 5.4225 kPa, 7.4525 N/m2 and 5.9256kg, 7.0484 kPa, 11.9012 N/m2 respectively. These values are substituted into MSM, which is then visualized in CHAI 3D ensuring the performance required by a real time simulation. Finally, comparison between the liver with and without tumor in terms of mass, spring stiffness, and damping constant is highlighted.

This paper aims to enhance the current contour and corner detection approach by applying smoothing and adaptive thresholding techniques to the stream input and then use subpixel corner detection to obtain better and more accurate interest point. There are two main steps involved in AR application, first-detect and extract local features and secondvisualization and rendering. Our focus is the first part of the whole operationfeatures. We proposed marker-less approach as to avoid the needs to prepare the target environment and to make our approach more flexible. The proposed method starts with first getting an input from the real environment thru a camera as visual sensor. On receiving an input image, the proposed system will process the image, finds and detects strong interest point from the ROI by applying enhanced contour-corner detection. From the ROI, features such as number of corners and vertices can be extracted and later can be used to determine a marker. For testing purposes, a mannequin as an input is used. Based on the experiment, the proposed method manage to capture the environment, convert captured frame into grey-scale image, detect corners and contours and also able to identify and verify a marker. 

Hand-drawn square-ROI detector was developed as one of the vital components in Real-Time Pre-Placed Markerless Square-ROI (RPMS) recognition technique. It aims to; 1. To verify hand-drawn Square-ROI (Region of Interest) as a square, and 2. To create a robust and flexible square-ROI detector technique which can be applied in uneven lighting condition. In this paper, we aim to detect only the desired ROI and handle the uneven lighting condition which is one of the primary disturbance sources that may generate false results. This may lead to error in registration in Augmented Reality application due to inability to correctly define a marker. As a solution, our technique applies adaptive thresholding in order to address this issue and to create a robust and flexible technique. To verify our proposed technique, two kinds of square is used in the testing and evaluation phase. In this experiment, two influencing factors; viewing distance, and detection accuracy were used to validate our aim. The results of the experiments show that the proposed technique efficiently detects and defines the desired square-ROI and also robust to illumination changes.

Abstract. Mass Spring Model (MSM) is a highly efficient model in terms of calculations and easy implementation. Mass, spring stiffness coefficient and damping constant are three major components of MSM. This paper focuses on identifying the coefficients of spring stiffness and damping constant using automated tuning method by optimization in generating human liver model capable of responding quickly. To achieve the objective two heuristic approaches are used, namely Simulated Annealing (SA) and Genetic Algorithm (GA) on the human liver model data set. The properties of the mechanical heart, which are taken into consideration, are anisotropy and viscoelasticity. Optimization results from SA and GA are then implemented into the MSM to model two human hearts, each with its SA or GA construction parameters. These techniques are implemented while making FEM construction parameters as benchmark.Step size response of both models are obtained after MSMs were solved using Fourth Order Runge-Kutta (RK4) to compare the elasticity response of both models. Remodelled time using manual calculation methods was compared against heuristic optimization methods of SA and GA in showing that model with automatic construction is more realistic in terms of realtime interaction response time. Liver models generated using SA and GA optimization techniques are compared with liver model from manual calculation. It shows that the reconstruction time required for 1000 repetitions of SA and GA is faster than the manual method. Meanwhile comparison between construction time of SA and GA model indicates that model SA is faster than GA with varying rates of time 0.110635 seconds/1000 repetitions. Real-time interaction of mechanical properties is dependent on rate of time and speed of remodelling process. Thus, the SA and GA have proven to be suitable in enhancing realism of simulated real-time interaction in liver remodelling.