Tissue Extraction from Ultrasonic Image by Prediction Filtering
Summary :
An image obtained by ultrasonic medical equipment is poor in quality because of speckle noise, that is caused by the quality of ultrasonic beam and so on. Thus, it is very difficult to detect internal organs or the diseased tissues from a medical ultrasonic image by the processing, which is used only gray-scale of the image. To analyze the ultrasonic image, it is necessary to use not only gray-scale but also appropriate statistical character. In this paper, we suggest a new method to extract regions of internal organs from an ultrasonic image by the discrimination function. The discrimination function is based on gray-scale and statistical characters of the image. This function is determined by using parameters of the multi-dimensional autoregressive model.
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- IEICE TRANSACTIONS on Fundamentals Vol.E79-A No.8 pp.1194-1201
- Publication Date
- 1996/08/25
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- Special Section PAPER (Special Section on Digital Signal Processing)
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Atsushi TAKEMURA, Masayasu ITO, "Tissue Extraction from Ultrasonic Image by Prediction Filtering" in IEICE TRANSACTIONS on Fundamentals,
vol. E79-A, no. 8, pp. 1194-1201, August 1996, doi: .
Abstract: An image obtained by ultrasonic medical equipment is poor in quality because of speckle noise, that is caused by the quality of ultrasonic beam and so on. Thus, it is very difficult to detect internal organs or the diseased tissues from a medical ultrasonic image by the processing, which is used only gray-scale of the image. To analyze the ultrasonic image, it is necessary to use not only gray-scale but also appropriate statistical character. In this paper, we suggest a new method to extract regions of internal organs from an ultrasonic image by the discrimination function. The discrimination function is based on gray-scale and statistical characters of the image. This function is determined by using parameters of the multi-dimensional autoregressive model.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e79-a_8_1194/_p
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@ARTICLE{e79-a_8_1194,
author={Atsushi TAKEMURA, Masayasu ITO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Tissue Extraction from Ultrasonic Image by Prediction Filtering},
year={1996},
volume={E79-A},
number={8},
pages={1194-1201},
abstract={An image obtained by ultrasonic medical equipment is poor in quality because of speckle noise, that is caused by the quality of ultrasonic beam and so on. Thus, it is very difficult to detect internal organs or the diseased tissues from a medical ultrasonic image by the processing, which is used only gray-scale of the image. To analyze the ultrasonic image, it is necessary to use not only gray-scale but also appropriate statistical character. In this paper, we suggest a new method to extract regions of internal organs from an ultrasonic image by the discrimination function. The discrimination function is based on gray-scale and statistical characters of the image. This function is determined by using parameters of the multi-dimensional autoregressive model.},
keywords={},
doi={},
ISSN={},
month={August},}
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TY - JOUR
TI - Tissue Extraction from Ultrasonic Image by Prediction Filtering
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1194
EP - 1201
AU - Atsushi TAKEMURA
AU - Masayasu ITO
PY - 1996
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E79-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 1996
AB - An image obtained by ultrasonic medical equipment is poor in quality because of speckle noise, that is caused by the quality of ultrasonic beam and so on. Thus, it is very difficult to detect internal organs or the diseased tissues from a medical ultrasonic image by the processing, which is used only gray-scale of the image. To analyze the ultrasonic image, it is necessary to use not only gray-scale but also appropriate statistical character. In this paper, we suggest a new method to extract regions of internal organs from an ultrasonic image by the discrimination function. The discrimination function is based on gray-scale and statistical characters of the image. This function is determined by using parameters of the multi-dimensional autoregressive model.
ER -
English
