Process Automation in extraction of 3D Models from Medical Images for VR and Haptic Simulations

Process automation in developing digital 3D models for Immersive touch is presented in this thesis. Immersive touch is a surgical virtual reality platform which provides simultaneous graphic and haptic rendering to see, feel and experience a minimally invasive surgical pathway. For simulating this surgical experience, 3D models of various anatomies must be obtained from medical imaging information such as Computer Tomography (CT) and Magnetic Resonance Imaging (MRI). This thesis details the algorithms developed to automate and thereby optimize multiple areas of the preprocessing stage in the product development pipeline. Areas for which the algorithms were developed includes organizing input data, creating 3D models by image segmentation and post segmentation model optimization by mesh reduction. The segmentation process is automated by developing various methods like Volumetric approach, Manual centering method and Hough transform method to be used depending on the type of input data whereas a single model optimization algorithm developed works on all datasets. Implementing these algorithms has helped creating a standard operating procedure and reduced the overall time required by 56 percent while maintaining the same accuracy as manual work.