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Real-time Knowledge-based Fuzzy Logic Model for Soft Tissue Deformation

  • Book
  • © 2019

Overview

Authors:
  1. Joey Sing Yee Tan

    1. Complexity Institute, Nanyang Technological University, Singapore, Singapore

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  2. Amandeep S. Sidhu

    1. Biological Mapping Research Institute (BIOMAP), Perth, Australia

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    You can also search for this author in PubMed   Google Scholar

  • Presents an improved mass spring model to simulate soft tissue deformation
  • The first specific contribution on liver modeling using a real-time and knowledge-based fuzzy mass spring model
  • Provides a real-time and knowledge-based fuzzy logic model for soft tissue deformation

Part of the book series: Studies in Computational Intelligence (SCI, volume 832)

Part of the book sub series: Data, Semantics and Cloud Computing (DSCC)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-ix
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  2. Introduction

    • Joey Sing Yee Tan, Amandeep S. Sidhu
    Pages 1-10

  3. Background

    • Joey Sing Yee Tan, Amandeep S. Sidhu
    Pages 11-30

  4. Methodology

    • Joey Sing Yee Tan, Amandeep S. Sidhu
    Pages 31-47

  5. Fuzzy Inference System

    • Joey Sing Yee Tan, Amandeep S. Sidhu
    Pages 49-61

  6. CUDA Implementation

    • Joey Sing Yee Tan, Amandeep S. Sidhu
    Pages 63-67

  7. Conclusion

    • Joey Sing Yee Tan, Amandeep S. Sidhu
    Pages 69-70

  8. Back Matter

    Pages 71-88
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Keywords

About this book

This book provides a real-time and knowledge-based fuzzy logic model for soft tissue deformation. The demand for surgical simulation continues to grow, as there is a major bottleneck in surgical simulation designation and every patient is unique. Deformable models, the core of surgical simulation, play a crucial role in surgical simulation designation. Accordingly, this book (1) presents an improved mass spring model to simulate soft tissue deformation for surgery simulation; (2) ensures the accuracy of simulation by redesigning the underlying Mass Spring Model (MSM) for liver deformation, using three different fuzzy knowledge-based approaches to determine the parameters of the MSM; (3) demonstrates how data in Central Processing Unit (CPU) memory can be structured to allow coalescing according to a set of Graphical Processing Unit (GPU)-dependent alignment rules; and (4) implements heterogeneous parallel programming for the distribution of grid threats for Computer Unified Device Architecture (CUDA)-based GPU computing. 

Authors and Affiliations

  • Complexity Institute, Nanyang Technological University, Singapore, Singapore

    Joey Sing Yee Tan

  • Biological Mapping Research Institute (BIOMAP), Perth, Australia

    Amandeep S. Sidhu

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