DocumentCode
348785
Title
Computed tomography based on a self-organizing neural network
Author
Monma, Hiroaki ; Chen, Yen-wei ; Nakao, Zensho
Author_Institution
Fac. of Eng., Ryukyus Univ., Okinawa, Japan
Volume
4
fYear
1999
fDate
1999
Firstpage
895
Abstract
We propose a method based on a self-organizing neural network (SONN) for computed tomography (CT). An expectation maximization-maximum likelihood algorithm, which is a well-known method for CT, is used as a learning algorithm of the network. The network is trained to minimize the Euclidean distance between the obtained projections and the projections of the estimate. Since the SONN starts with many different estimates, it is easy to obtain a global optimum
Keywords
computerised tomography; image reconstruction; learning (artificial intelligence); maximum likelihood estimation; self-organising feature maps; Euclidean distance; computed tomography; expectation maximization-maximum likelihood algorithm; global optimum; learning algorithm; self-organizing neural network; Computed tomography; Computer networks; Electronic mail; Euclidean distance; Image reconstruction; Iterative algorithms; Iterative methods; Neural networks; Neurons; Organizing;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on
Conference_Location
Tokyo
ISSN
1062-922X
Print_ISBN
0-7803-5731-0
Type
conf
DOI
10.1109/ICSMC.1999.812528
Filename
812528
Link To Document