Quantitative Diagnosis on Magnetic Resonance Images of Chronic Liver Disease Using Neural Networks
Summary :
We have classified parenchymal echo patterns of cirrhotic liver into 3 types, according to the size of hypoechoic nodular lesions. We have been studying an ultrasonic image diagnosis system using the three–layer back–propagation neural network. In this paper, we will describe the applications of the neural network techniques for recognizing and classifying chronic liver disease, which use the nodular lesions in the Proton density and T2–weighed magnetic resonance images on the gray level of the pixels in the region of interest.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E77-A No.11 pp.1846-1850
- Publication Date
- 1994/11/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section on Nonlinear Theory and Its Applications)
- Category
- Neural Network and Its Applications
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Shin'ya YOSHINO, Akira KOBAYASHI, Takashi YAHAGI, Hiroyuki FUKUDA, Masaaki EBARA, Masao OHTO, "Quantitative Diagnosis on Magnetic Resonance Images of Chronic Liver Disease Using Neural Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E77-A, no. 11, pp. 1846-1850, November 1994, doi: .
Abstract: We have classified parenchymal echo patterns of cirrhotic liver into 3 types, according to the size of hypoechoic nodular lesions. We have been studying an ultrasonic image diagnosis system using the three–layer back–propagation neural network. In this paper, we will describe the applications of the neural network techniques for recognizing and classifying chronic liver disease, which use the nodular lesions in the Proton density and T2–weighed magnetic resonance images on the gray level of the pixels in the region of interest.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e77-a_11_1846/_p
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@ARTICLE{e77-a_11_1846,
author={Shin'ya YOSHINO, Akira KOBAYASHI, Takashi YAHAGI, Hiroyuki FUKUDA, Masaaki EBARA, Masao OHTO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Quantitative Diagnosis on Magnetic Resonance Images of Chronic Liver Disease Using Neural Networks},
year={1994},
volume={E77-A},
number={11},
pages={1846-1850},
abstract={We have classified parenchymal echo patterns of cirrhotic liver into 3 types, according to the size of hypoechoic nodular lesions. We have been studying an ultrasonic image diagnosis system using the three–layer back–propagation neural network. In this paper, we will describe the applications of the neural network techniques for recognizing and classifying chronic liver disease, which use the nodular lesions in the Proton density and T2–weighed magnetic resonance images on the gray level of the pixels in the region of interest.},
keywords={},
doi={},
ISSN={},
month={November},}
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TY - JOUR
TI - Quantitative Diagnosis on Magnetic Resonance Images of Chronic Liver Disease Using Neural Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1846
EP - 1850
AU - Shin'ya YOSHINO
AU - Akira KOBAYASHI
AU - Takashi YAHAGI
AU - Hiroyuki FUKUDA
AU - Masaaki EBARA
AU - Masao OHTO
PY - 1994
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E77-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 1994
AB - We have classified parenchymal echo patterns of cirrhotic liver into 3 types, according to the size of hypoechoic nodular lesions. We have been studying an ultrasonic image diagnosis system using the three–layer back–propagation neural network. In this paper, we will describe the applications of the neural network techniques for recognizing and classifying chronic liver disease, which use the nodular lesions in the Proton density and T2–weighed magnetic resonance images on the gray level of the pixels in the region of interest.
ER -
English
