Neural Network Approach to Characterization of Cirrhotic Parenchymal Echo Patterns
Summary :
We have calssified parenchymal echo patterns of cirrhotic liver into four types, according to the size of hypoechoic nodular lesions. Neural network technique has been applied to the characterization of hepatic parenchymal diseases in ultrasonic B-scan texture. We employed a multi-layer feedforward neural network utilizing the back-propagation algorithm. We carried out four kinds of pre-processings for liver parenchymal pattern in the images. We describe the examination of each performance by these pre-processing techniques. We show four results using (1) only magnitudes of FFT pre-processing, (2) both magnitudes and phase angles, (3) data normalized by the maximum value in the dataset, and (4) data normalized by variance of the dataset. Among the 4 pre-processing data treatments studied, the process combining FFT phase angles and magnitudes of FFT is found to be the most efficient.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E76-A No.8 pp.1316-1322
- Publication Date
- 1993/08/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section of Papers Selected from the 7th Digital Signal Processing Symposium)
- Category
- Biomedical Signal Processing
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Shin-ya YOSHINO, Akira KOBAYASHI, Takashi YAHAGI, Hiroyuki FUKUDA, Masaaki EBARA, Masao OHTO, "Neural Network Approach to Characterization of Cirrhotic Parenchymal Echo Patterns" in IEICE TRANSACTIONS on Fundamentals,
vol. E76-A, no. 8, pp. 1316-1322, August 1993, doi: .
Abstract: We have calssified parenchymal echo patterns of cirrhotic liver into four types, according to the size of hypoechoic nodular lesions. Neural network technique has been applied to the characterization of hepatic parenchymal diseases in ultrasonic B-scan texture. We employed a multi-layer feedforward neural network utilizing the back-propagation algorithm. We carried out four kinds of pre-processings for liver parenchymal pattern in the images. We describe the examination of each performance by these pre-processing techniques. We show four results using (1) only magnitudes of FFT pre-processing, (2) both magnitudes and phase angles, (3) data normalized by the maximum value in the dataset, and (4) data normalized by variance of the dataset. Among the 4 pre-processing data treatments studied, the process combining FFT phase angles and magnitudes of FFT is found to be the most efficient.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e76-a_8_1316/_p
Copy
@ARTICLE{e76-a_8_1316,
author={Shin-ya YOSHINO, Akira KOBAYASHI, Takashi YAHAGI, Hiroyuki FUKUDA, Masaaki EBARA, Masao OHTO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Neural Network Approach to Characterization of Cirrhotic Parenchymal Echo Patterns},
year={1993},
volume={E76-A},
number={8},
pages={1316-1322},
abstract={We have calssified parenchymal echo patterns of cirrhotic liver into four types, according to the size of hypoechoic nodular lesions. Neural network technique has been applied to the characterization of hepatic parenchymal diseases in ultrasonic B-scan texture. We employed a multi-layer feedforward neural network utilizing the back-propagation algorithm. We carried out four kinds of pre-processings for liver parenchymal pattern in the images. We describe the examination of each performance by these pre-processing techniques. We show four results using (1) only magnitudes of FFT pre-processing, (2) both magnitudes and phase angles, (3) data normalized by the maximum value in the dataset, and (4) data normalized by variance of the dataset. Among the 4 pre-processing data treatments studied, the process combining FFT phase angles and magnitudes of FFT is found to be the most efficient.},
keywords={},
doi={},
ISSN={},
month={August},}
Copy
TY - JOUR
TI - Neural Network Approach to Characterization of Cirrhotic Parenchymal Echo Patterns
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1316
EP - 1322
AU - Shin-ya YOSHINO
AU - Akira KOBAYASHI
AU - Takashi YAHAGI
AU - Hiroyuki FUKUDA
AU - Masaaki EBARA
AU - Masao OHTO
PY - 1993
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E76-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 1993
AB - We have calssified parenchymal echo patterns of cirrhotic liver into four types, according to the size of hypoechoic nodular lesions. Neural network technique has been applied to the characterization of hepatic parenchymal diseases in ultrasonic B-scan texture. We employed a multi-layer feedforward neural network utilizing the back-propagation algorithm. We carried out four kinds of pre-processings for liver parenchymal pattern in the images. We describe the examination of each performance by these pre-processing techniques. We show four results using (1) only magnitudes of FFT pre-processing, (2) both magnitudes and phase angles, (3) data normalized by the maximum value in the dataset, and (4) data normalized by variance of the dataset. Among the 4 pre-processing data treatments studied, the process combining FFT phase angles and magnitudes of FFT is found to be the most efficient.
ER -
English
