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Neural network shape: Organ shape representation with radial basis function neural networks

  • Guoyu Lu
  • , Li Ren
  • , Abhishek Kolagunda
  • , Chandra Kambhamettu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

We propose to represent the shape of an organ using a neural network classifier. The shape is represented by a function learned by a neural network. Radial Basis Function (RBF) is used as the activation function for each perceptron. The learned implicit function is a combination of radial basis functions, which can represent complex shapes. The organ shape representation is learned using classification methods. Our testing results show that the neural network shape provides the best representation accuracy. The use of RBF provides a rotation, translation and scaling invariant feature to represent the shape. Experiments show that our method can accurately represent the organ shape.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages932-936
Number of pages5
ISBN (Electronic)9781479999880
DOIs
StatePublished - May 18 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: Mar 20 2016Mar 25 2016

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2016-May

Conference

Conference41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Country/TerritoryChina
CityShanghai
Period03/20/1603/25/16

Keywords

  • 3D Reconstruction
  • Artificial Neural Network
  • RBF Kernel
  • Shape Presentation

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