Variational computing based segmentation methods for medical imaging by using CNN

Alexandru Gacsádi, Péter Szolgay

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

9 Citations (Scopus)

Abstract

The paper presents a new variational computing based medical image segmentation method by using Cellular Neural Networks (CNN). By implementing the proposed algorithm on FPGA (Field Programmable Gate Array) with an emulated digital CNN-UM (CNN-Universal Machine) there is the possibility to meet the requirements for medical image segmentation.

Original languageEnglish
Title of host publication2010 12th International Workshop on Cellular Nanoscale Networks and their Applications, CNNA 2010
Publication statusPublished - May 21 2010
Event2010 12th International Workshop on Cellular Nanoscale Networks and their Applications, CNNA 2010 - Berkeley, CA, United States
Duration: Feb 3 2010Feb 5 2010

Publication series

Name2010 12th International Workshop on Cellular Nanoscale Networks and their Applications, CNNA 2010

Other

Other2010 12th International Workshop on Cellular Nanoscale Networks and their Applications, CNNA 2010
CountryUnited States
CityBerkeley, CA
Period2/3/102/5/10

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Keywords

  • Cellular neural networks
  • Medical imaging
  • Segmentation
  • Variational computing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Cite this

Gacsádi, A., & Szolgay, P. (2010). Variational computing based segmentation methods for medical imaging by using CNN. In 2010 12th International Workshop on Cellular Nanoscale Networks and their Applications, CNNA 2010 [5430256] (2010 12th International Workshop on Cellular Nanoscale Networks and their Applications, CNNA 2010).