Title :
Variational computing based segmentation methods for medical imaging by using CNN
Author :
Gacsadi, A. ; Szolgay, P.
Author_Institution :
Electron. Dept., Univ. of Oradea, Oradea, Romania
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.
Keywords :
cellular neural nets; field programmable gate arrays; image segmentation; medical image processing; variational techniques; cellular neural networks; digital CNN-universal machine; field programmable gate array; medical image segmentation; medical imaging; variational computing; Application software; Biomedical imaging; Cellular networks; Cellular neural networks; Computed tomography; Computer networks; Image segmentation; Magnetic resonance imaging; Medical diagnostic imaging; Optimization methods; cellular neural networks; medical imaging; segmentation; variational computing;
Conference_Titel :
Cellular Nanoscale Networks and Their Applications (CNNA), 2010 12th International Workshop on
Conference_Location :
Berkeley, CA
Print_ISBN :
978-1-4244-6679-5
DOI :
10.1109/CNNA.2010.5430256
