Knowledge-based fuzzy approach in determining the mass spring stiffness parameter

Abstract

Mass spring model (MSM) has been used to represent several deformable objects in the graphical studies such as cloth simulation, face animation, body movement and hair simulation. Due to the ease of implementation of MSM, the context of MSM has been extended to surgical simulation, especially the real-time invasive surgical simulator. However, the remaining major drawback of MSM is the selection of parameters – stiffness () and damping (). Most of the researches have been focused on improving the MSM in terms of mathematical modelling, mesh topology and several other methods in obtaining the parameters either by using a white box, a black box or grey systems. In this paper, the implementation of fuzzy approach is introduced into liver surgical simulation by using MATLAB R2012, a fuzzy logic toolbox. We thus designed a fuzzy MSM stiffness controller (FMSC), which is a knowledge-based fuzzy inference system. Based on the previous medical stiffness outcome for several liver ailments, the rule matrix is built. Max–min (Mamdani) fuzzification method and centre of gravity defuzzification method are implemented. Users could tune on the FMSC based on the patient's ailments. The results show that the outcome of the stiffness value obtained from FMSC share the similar trend with the benchmark model. The gradient of each graphs generated from the FMSC data is directly proportional to the benchmark's graph. Therefore, it can be concluded that FMSC could be used for tuning the stiffness parameter.