A Computer-Aided System for Discrimination of Dilated Cardiomyopathy Using Echocardiographic Images

Du-Yih TSAI; Masaaki TOMITA

A Computer-Aided System for Discrimination of Dilated Cardiomyopathy Using Echocardiographic Images

Du-Yih TSAI, Masaaki TOMITA

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In this paper, the discrimination of ultrasonic heart (echocardiographic) images is studied by making use of some texture features, including the angular second moment, contrast, correlation and entropy which are obtained from a gray-level cooccurrence matrix. Features of these types are used as inputs to the input layer of a neural network (NN) to classify two sets of echocardiographic images-normal heart and dilated cardiomyopathy (DCM) (18 and 13 samples, respectively). The performance of the NN classifier is also compared to that of a minimum distance (MD) classifier. Implementation of our algorithm is performed on a PC-486 personal computer. Our results show that the NN produces about 94% (the confidence level setting is 0.9) and the MD produces about 84% correct classification. We notice that the NN correctly classifies all the DCM cases, namely, all the misclassified cases are of false positive. These results indicate that the method of feature-based image analysis using the NN has potential utility for computer-aided diagnosis of the DCM and other heart diseases.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E78-A No.12 pp.1649-1654

Publication Date: 1995/12/25

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Type of Manuscript: Special Section PAPER (Special Section on Acoustic Diagnosis)

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Du-Yih TSAI, Masaaki TOMITA, "A Computer-Aided System for Discrimination of Dilated Cardiomyopathy Using Echocardiographic Images" in IEICE TRANSACTIONS on Fundamentals, vol. E78-A, no. 12, pp. 1649-1654, December 1995, doi: .
Abstract: In this paper, the discrimination of ultrasonic heart (echocardiographic) images is studied by making use of some texture features, including the angular second moment, contrast, correlation and entropy which are obtained from a gray-level cooccurrence matrix. Features of these types are used as inputs to the input layer of a neural network (NN) to classify two sets of echocardiographic images-normal heart and dilated cardiomyopathy (DCM) (18 and 13 samples, respectively). The performance of the NN classifier is also compared to that of a minimum distance (MD) classifier. Implementation of our algorithm is performed on a PC-486 personal computer. Our results show that the NN produces about 94% (the confidence level setting is 0.9) and the MD produces about 84% correct classification. We notice that the NN correctly classifies all the DCM cases, namely, all the misclassified cases are of false positive. These results indicate that the method of feature-based image analysis using the NN has potential utility for computer-aided diagnosis of the DCM and other heart diseases.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e78-a_12_1649/_p

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@ARTICLE{e78-a_12_1649,
author={Du-Yih TSAI, Masaaki TOMITA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Computer-Aided System for Discrimination of Dilated Cardiomyopathy Using Echocardiographic Images},
year={1995},
volume={E78-A},
number={12},
pages={1649-1654},
abstract={In this paper, the discrimination of ultrasonic heart (echocardiographic) images is studied by making use of some texture features, including the angular second moment, contrast, correlation and entropy which are obtained from a gray-level cooccurrence matrix. Features of these types are used as inputs to the input layer of a neural network (NN) to classify two sets of echocardiographic images-normal heart and dilated cardiomyopathy (DCM) (18 and 13 samples, respectively). The performance of the NN classifier is also compared to that of a minimum distance (MD) classifier. Implementation of our algorithm is performed on a PC-486 personal computer. Our results show that the NN produces about 94% (the confidence level setting is 0.9) and the MD produces about 84% correct classification. We notice that the NN correctly classifies all the DCM cases, namely, all the misclassified cases are of false positive. These results indicate that the method of feature-based image analysis using the NN has potential utility for computer-aided diagnosis of the DCM and other heart diseases.},
keywords={},
doi={},
ISSN={},
month={December},}

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TY - JOUR
TI - A Computer-Aided System for Discrimination of Dilated Cardiomyopathy Using Echocardiographic Images
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1649
EP - 1654
AU - Du-Yih TSAI
AU - Masaaki TOMITA
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E78-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 1995
AB - In this paper, the discrimination of ultrasonic heart (echocardiographic) images is studied by making use of some texture features, including the angular second moment, contrast, correlation and entropy which are obtained from a gray-level cooccurrence matrix. Features of these types are used as inputs to the input layer of a neural network (NN) to classify two sets of echocardiographic images-normal heart and dilated cardiomyopathy (DCM) (18 and 13 samples, respectively). The performance of the NN classifier is also compared to that of a minimum distance (MD) classifier. Implementation of our algorithm is performed on a PC-486 personal computer. Our results show that the NN produces about 94% (the confidence level setting is 0.9) and the MD produces about 84% correct classification. We notice that the NN correctly classifies all the DCM cases, namely, all the misclassified cases are of false positive. These results indicate that the method of feature-based image analysis using the NN has potential utility for computer-aided diagnosis of the DCM and other heart diseases.
ER -