1Faculty of Mechanical Engineering, University of Guilan, Rasht, Iran
2K.N. Toosi University of Technology
Soft tissue modeling for the creation of a haptic simulator for training medical skills has been the focus of many attempts up to now. In soft tissue modeling the most important parameter considered is its being real-time, as well as its accuracy and sensitivity. In this paper, ANFIS approach is used to present a nonlinear model for soft tissue. The required data for training the neuro-fuzzy model of soft tissue is provided from breast tissue numerical modeling in ANSYS 12.0 software. To validate the ANSYS mode, numerical data have been compared with the experimental data with an average error of less than 3%. On the other hand, for the validation of ANFIS model, testing session indicates a root mean square error of less than 0.02 (N), which shows the high degree of accuracy for the presented model. To evaluate the efficiency of this model, it has been used in the breast cancerous tumors diagnosis training haptic simulator. The presented model’s real-time feature is about 100 times more than the maximum amount needed for force modeling simulations.
 A. Jemal, R. Siegel, E. Ward, Y. Hao, J. Xu, M. J. Thun, “Cancer statistics,” CA: a cancer journal for clinicians, vol. 59, no. 4, pp. 225–249, 2009.
 D. Saslow, J. Hannan, J. Osuch, M. H. Alciati, C. Baines, M. Barton, J. K. Bobo, C. Coleman, M. Dolan, G. Gaumer, et al., “Clinical breast examination: practical recommendations for optimizing performance and reporting,” CA: A Cancer Journal for Clinicians, vol. 54, no. 6, pp. 327–344, 2004.
 H. Abbasi, S. Haghzad, S. Amirkhani, and M. Eshghi, "Early detection of breast cancer using genetic algorithm and Neuro-Fuzzy network," in 2014 22nd Iranian Conference on Electrical Engineering (ICEE), 2014, pp. 1939-1944.
 R. M. Satava, “Virtual reality surgical simulator, Surgical endoscopy 7,” vol. 3, pp. 203–205, 1993.
 J. D. Westwood, “Anatomical and physiological models for surgical simulation,” Medicine Meets Virtual Reality: The Convergence of Physical & Informational Technologies: Options for a New Era in Healthcare, vol. 62, pp. 23, 1999.
 L. M. Sutherland, P. F. Middleton, A. Anthony, J. Hamdorf, P. Cregan, D. Scott, G. J. Maddern, “Surgical simulation: a systematic review,” Annals of surgery, vol. 243, no. 3, pp. 291, 2006.
 S. Jeon, B. Knoerlein, M. Harders, S. Choi, “Haptic simulation of breast cancer palpation: A case study of haptic augmented reality,” in: Mixed and Augmented Reality (ISMAR), 9th IEEE International Symposium on IEEE, pp. 237–238, 2010.
 M. Solanki, V. Raja, “Modelling palpable masses for a virtual breast examination,” in: Proceedings of the 9th ACM SIGGRAPH Conference on Virtual Reality Continuum and its Applications in Industry, ACM, pp. 221–224, 2011.
 J. Akin, Application and implementation of finite element methods, Academic Press, Inc., 1982.
 S. Niroomandi, I. Alfaro Ruiz, E. Cueto Prendes, Real-time simulation of surgery by model reduction and x-fem techniques. Centro Politécnico Superior, Universidad de Zaragoza, 2011.
 G. Picinbono, H. Delingette, N. Ayache, “Non-linear anisotropic elasticity for real-time surgery simulation,” Graphical Models, vol. 65, no. 5, pp. 305–321, 2003.
 M. Bro-Nielsen, “Finite element modeling in surgery simulation,” Proceedings of the IEEE, vol. 86, no. 3, pp. 490–503, 1998.
 S. Niroomandi, I. Alfaro, E. Cueto, and F. Chinesta, "Accounting for large deformations in real-time simulations of soft tissues based on reduced-order models," Computer Methods and Programs in Biomedicine, vol. 105, pp. 1-12, 2012.
 H. Courtecuisse, J. Allard, P. Kerfriden, S. P. Bordas, S. Cotin, and C. Duriez, "Real-time simulation of contact and cutting of heterogeneous soft-tissues," Medical image analysis, vol. 18, pp. 394-410, 2014.
 D. González, I. Alfaro, C. Quesada, E. Cueto, and F. Chinesta, "Computational vademecums for the real-time simulation of haptic collision between nonlinear solids," Computer Methods in Applied Mechanics and Engineering, vol. 283, pp. 210-223, 2015.
 W. Song, K. Yuan, Y. Fu, “Haptic modeling and rendering based on neurofuzzy rules for surgical cutting simulation,” Acta Automatica Sinica, vol. 32, no. 2, pp. 193, 2006.
 K. Salisbury, D. Brock, T. Massie, N. Swarup, C. Zilles, “Haptic rendering: Programming touch interaction with virtual objects,” in: Proceedings of symposium on Interactive 3D graphics, ACM, pp. 123–130, 1995.
 K. Salisbury, F. Conti, F. Barbagli, “Haptic rendering: introductory concepts,” Computer Graphics and Applications, IEEE, vol. 24, no. 2, pp. 24–32, 2004.
 D. C. Ruspini, K. Kolarov, O. Khatib, “The haptic display of complex graphical environments,” in: Proceedings of the 24th annual conference on Computer graphics and interactive techniques, ACM Press/Addison-Wesley Publishing Co., pp. 345–352, 1997.
 S. Amirkhani and A. Nahvi, "Design and implementation of an interactive virtual control laboratory using haptic interface for undergraduate engineering students," Computer Applications in Engineering Education, 2016.
 C. B. Zilles, Haptic rendering with the toolhandle haptic interface, Ph.D. thesis, Citeseer, 1995.
 H. Konig, T. Strothotte, O. von Guericke, “Fast collision detection for haptic displays using polygonal models,” in: SimVis, Citeseer, pp. 289–300, 2002.
 J. S. Jang, C.-T. Sun, Neuro-fuzzy modeling and control, Proceedings of the IEEE, vol. 83, no. 3, pp. 378–406,1995.
 J. S. Jang, Anfis: adaptive-network-based fuzzy inference system, Systems, Man and Cybernetics, IEEE Transactions on, vol. 23, no. 3, pp. 665–685, 1993.
 T. A. Krouskop, T. M. Wheeler, F. Kallel, B. S. Garra, T. Hall, Elastic moduli of breast and prostate tissues under compression, Ultrasonic imaging, vol. 20, no. 4, pp. 260–274, 1998.
 S. Martin and N. Hillier, Characterisation of the Novint Falcon haptic device for application as a robot manipulator, in Australasian Conference on Robotics and Automation (ACRA), pp. 291-292, 2009.