Abstract—Typical biomedical model transformed from CT scan and MRI images involved features suppression during the conversion to CAE (Computer Aided Engineering) model. However, most of biomedical application may post significant impact if minor features are suppressed or neglected during the conversion process. Thus, reverse engineering and CAD (Computer Aided Design) technology play a central role in today’s multidisciplinary simulation environments. Most notably CAD models now contain many details which are irrelevant to simulation disciplines. CAD systems contain feature trees which record the features creation but not specific to its use in biomedical applications. Many features of little significance to an analysis only emerge during the construction of the model. The ability to selectively suppress and reinstate features while maintaining an audit trail of changes is required to facilitate the control of the idealisation process. This work explores CAD model for biomedical applications to examine its small features that are useful or needed in the CAE so that CAD model simplification operations can be designed as continuous transformations. Irrelevant features can then be suppressed and subsequently reinstated, within defined limitations, independently from the order in which they were suppressed.
Index Terms—Feature suppression, feature preservation, FEA, biomedical application
X. Wang was with the Universiti Tunku Abdul Rahman, 53300, Kuala Lumpur, Malaysia. She is now with School of Enigneering, Monash Univesity Sunway Campus (e-mail:emwangxin@gmail.com).
C. S. Tan and H. Y. Wong aer with Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Selangor, Malaysia (e-mail : cstan@mmu.edu.my, hywong@mmu.edu.my).
K. Y. Lee was with the Universiti Tunku Abdul Rahman, 53300, Kuala Lumpur, Malaysia. He is now in the industry (e-mail:kokyong.lee@gmail.com).
Cite: Xin Wang, Ching Seong Tan, Kok Yong Lee, and Hin Yong Wong, "Feature Preservation Considerations for Metacarpals and Heart Related Modeling," International Journal of Modeling and Optimization vol. 2, no. 2, pp. 87-91, 2012.
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