A hybrid approach to detection of brain hemorrhage candidates from clinical head CT scans

Authors:
Yonghong Li;Qingmao Hu;Jianhuang Wu;Zhijun Chen
Affiliations:
Shenzhen Institute of Advanced Integration Technology, Chinese Academy of Sciences/ The Chinese University of Hong Kong;Shenzhen Institute of Advanced Integration Technology, Chinese Academy of Sciences/ The Chinese University of Hong Kong and Key Laboratory for Biomedical Informatics and Health Engineering, Chines ...;Shenzhen Institute of Advanced Integration Technology, Chinese Academy of Sciences/ The Chinese University of Hong Kong;Shenzhen Institute of Advanced Integration Technology, Chinese Academy of Sciences/ The Chinese University of Hong Kong
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Year:
2009

Citing 1
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A knowledge discovery approach to diagnosing intracranial hematomas on brain CT: recognition, measurement and classification

ICMB'08 Proceedings of the 1st international conference on Medical biometrics

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Abstract

In this paper we present an approach for detecting brain hemorrhage regions from clinical head computed tomography (CT) scans. Firstly, nonbrain tissues are removed by thresholding based on Fuzzy C-means (FCM) clustering. Then, thresholding based on maximum entropy is employed for the candidate hemorrhage region detection. Finally, nonhemorrhage regions and other normal artifacts are differentiated from hemorrhage regions by a knowledge-based classification system. The approach has been validated against 30 clinical brain CT images and compared with Otsu thresholding as well as hierarchical FCM thresholding.