TY - GEN
T1 - Predictors of Intracerebral Hematoma Enlargement Using Brain CT Images in Emergency Medical Care
AU - Oka, Kazunori
AU - Hirahara, Takumi
AU - Nohara, Yasunobu
AU - Inoue, Sozo
AU - Arimura, Koichi
AU - Kobashi, Syoji
AU - Iihara, Koji
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6/8
Y1 - 2021/6/8
N2 - Intracerebral hematoma (ICH) is the cause of intracerebral hemorrhage. Acute enlargement of the ICH is high risk, and emergency surgical treatment is required. Therefore, prediction of ICH enlargement is essential to improve a survival rate and outcome. The purpose of this study is to find factors to predict the ICH enlargement with thick slice head CT images. We propose three kinds of feature extraction methods, (1) shape and texture features, (2) layered texture features, and (3) anatomical location features. In addition, we introduce an ICH enlargement prediction method using support vector machine (SVM) and feature selection. The experimental results showed that the angular second order moment of the texture feature was the most effective in predicting the ICH enlargement. By using this feature, we were able to predict the ICH enlargement with an accuracy of 75.7%. In addition, we found that normalization of the location and posture improved the prediction accuracy by 2.7% compared to that without normalization.
AB - Intracerebral hematoma (ICH) is the cause of intracerebral hemorrhage. Acute enlargement of the ICH is high risk, and emergency surgical treatment is required. Therefore, prediction of ICH enlargement is essential to improve a survival rate and outcome. The purpose of this study is to find factors to predict the ICH enlargement with thick slice head CT images. We propose three kinds of feature extraction methods, (1) shape and texture features, (2) layered texture features, and (3) anatomical location features. In addition, we introduce an ICH enlargement prediction method using support vector machine (SVM) and feature selection. The experimental results showed that the angular second order moment of the texture feature was the most effective in predicting the ICH enlargement. By using this feature, we were able to predict the ICH enlargement with an accuracy of 75.7%. In addition, we found that normalization of the location and posture improved the prediction accuracy by 2.7% compared to that without normalization.
UR - http://www.scopus.com/inward/record.url?scp=85113766798&partnerID=8YFLogxK
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U2 - 10.1109/CYBCONF51991.2021.9464139
DO - 10.1109/CYBCONF51991.2021.9464139
M3 - Conference contribution
AN - SCOPUS:85113766798
T3 - 2021 5th IEEE International Conference on Cybernetics, CYBCONF 2021
SP - 24
EP - 29
BT - 2021 5th IEEE International Conference on Cybernetics, CYBCONF 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Cybernetics, CYBCONF 2021
Y2 - 8 June 2021 through 10 June 2021
ER -