A contextual-constraint based Hopfield neural cube for medical image segmentation

Chuan Yu Chang, Pau Choo Chung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Proposes a 3-D Hopfield neural network called Contextual-Constraint Based Hopfield Neural Cube (CCBHNC) taking both each single pixel's feature and its surrounding contextual information for image segmentation, mimicking a high-level vision system. Different from other neural networks, CCBHNC extends the two-dimensional Hopfield neural network into a three-dimensional Hopfield neural cube for it to easily take each pixel's surrounding contextual information into its network operation. As CCBHNC uses a high-level image segmentation model, disconnected fractions arising in the course of tiny details or noises will be effectively removed. Furthermore, the CCBHNC follows the competitive learning rule to update the neuron states, thus precluding the necessity of determining the values for the hard constraints in the energy function, which is usually required in a Hopfield neural network, and facilitating the energy function to converge fast. The simulation results indicate that CCBHNC can produce more continued, more intact, and smoother images in comparison with the other methods.

Original languageEnglish
Title of host publicationIEEE Region 10 Annual International Conference, Proceedings/TENCON
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1170-1173
Number of pages4
ISBN (Electronic)0780357396, 9780780357396
DOIs
Publication statusPublished - 1999 Jan 1
Event1999 IEEE Region 10 Conference, TENCON 1999 - Cheju Island, Korea, Republic of
Duration: 1999 Sep 151999 Sep 17

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume2
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Other

Other1999 IEEE Region 10 Conference, TENCON 1999
Country/TerritoryKorea, Republic of
CityCheju Island
Period99-09-1599-09-17

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

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