Automatic Detection of the Existence of Subarachnoid Hemorrhage from Clinical CT Images

  • Authors:

  • Yonghong Li;Jianhuang Wu;Hongwei Li;Degang Li;Xiaohua Du;Zhijun Chen;Fucang Jia;Qingmao Hu

  • Affiliations:

  • Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China and The Chinese University of Hong Kong, New Territories, Hong Kong and Institute of Computing Technology, ...;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China and The Chinese University of Hong Kong, New Territories, Hong Kong;Ningxia Medical University, Yinchuan, China;The Third Affiliated Hospital of Inner Mongolia Medical College, Baotou, China;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China and The Chinese University of Hong Kong, New Territories, Hong Kong;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China and The Chinese University of Hong Kong, New Territories, Hong Kong;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China and The Chinese University of Hong Kong, New Territories, Hong Kong;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China and The Chinese University of Hong Kong, New Territories, Hong Kong and Research Center for Human-Computer ...

  • Venue:
  • Journal of Medical Systems
  • Year:
  • 2012

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Abstract

Subarachnoid hemorrhage (SAH) is a medical emergency which can lead to death or severe disability. Misinterpretation of computed tomography (CT) in patients with SAH is a common problem. How to improve the accuracy of diagnosis is a great challenge to both the clinical physicians and medical researchers. In this paper we proposed a method for the automatic detection of SAH on clinical non-contrast head CT scans. The novelty includes approximation of the subarachnoid space in head CT using an atlas based registration, and exploration of support vector machine to the detection of SAH. The study included 60 patients with SAH and 69 normal controls from clinical hospitals. Thirty patients with SAH and 30 normal controls were used for training, while the rest were used for testing to achieve a testing sensitivity of 100% and specificity of 89.7%. The proposed algorithm might be a potential tool to screen the existence of SAH.