Feature-Based Image Analysis for Classification of Echocardiographic Images
Summary :
In this letter the classification of echocardiographic images is studied by making use of some texture features, including the angular second moment, the contrast, the correlation, and the entropy which are obtained from a gray-level cooccurrence matrix. Features of these types are used to classify two sets of echocardiographic images-normal and abnormal (cardiomyopathy) hearts. A minimum distance classifier and evaluation indexes are employed to evaluate the performance of these features. Implementation of our algorithm is performed on a PC-386 personal computer and produces about 87% correct classification for the two sets of echocardiographic images. Our preliminary results suggest that this method of feature-based image analysis has potential use for computer-aided diagnosis of heart diseases.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E78-A No.5 pp.589-593
- Publication Date
- 1995/05/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section LETTER (Special Section of Letters Selected from the 1994 IEICE Fall Conference)
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Du-Yih TSAI, Masaaki TOMITA, "Feature-Based Image Analysis for Classification of Echocardiographic Images" in IEICE TRANSACTIONS on Fundamentals,
vol. E78-A, no. 5, pp. 589-593, May 1995, doi: .
Abstract: In this letter the classification of echocardiographic images is studied by making use of some texture features, including the angular second moment, the contrast, the correlation, and the entropy which are obtained from a gray-level cooccurrence matrix. Features of these types are used to classify two sets of echocardiographic images-normal and abnormal (cardiomyopathy) hearts. A minimum distance classifier and evaluation indexes are employed to evaluate the performance of these features. Implementation of our algorithm is performed on a PC-386 personal computer and produces about 87% correct classification for the two sets of echocardiographic images. Our preliminary results suggest that this method of feature-based image analysis has potential use for computer-aided diagnosis of heart diseases.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e78-a_5_589/_p
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@ARTICLE{e78-a_5_589,
author={Du-Yih TSAI, Masaaki TOMITA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Feature-Based Image Analysis for Classification of Echocardiographic Images},
year={1995},
volume={E78-A},
number={5},
pages={589-593},
abstract={In this letter the classification of echocardiographic images is studied by making use of some texture features, including the angular second moment, the contrast, the correlation, and the entropy which are obtained from a gray-level cooccurrence matrix. Features of these types are used to classify two sets of echocardiographic images-normal and abnormal (cardiomyopathy) hearts. A minimum distance classifier and evaluation indexes are employed to evaluate the performance of these features. Implementation of our algorithm is performed on a PC-386 personal computer and produces about 87% correct classification for the two sets of echocardiographic images. Our preliminary results suggest that this method of feature-based image analysis has potential use for computer-aided diagnosis of heart diseases.},
keywords={},
doi={},
ISSN={},
month={May},}
Copy
TY - JOUR
TI - Feature-Based Image Analysis for Classification of Echocardiographic Images
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 589
EP - 593
AU - Du-Yih TSAI
AU - Masaaki TOMITA
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E78-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 1995
AB - In this letter the classification of echocardiographic images is studied by making use of some texture features, including the angular second moment, the contrast, the correlation, and the entropy which are obtained from a gray-level cooccurrence matrix. Features of these types are used to classify two sets of echocardiographic images-normal and abnormal (cardiomyopathy) hearts. A minimum distance classifier and evaluation indexes are employed to evaluate the performance of these features. Implementation of our algorithm is performed on a PC-386 personal computer and produces about 87% correct classification for the two sets of echocardiographic images. Our preliminary results suggest that this method of feature-based image analysis has potential use for computer-aided diagnosis of heart diseases.
ER -
English
