Automatic recognition of midline shift on brain CT images

Chun Chih Liao, Furen Xiao, Jau Min Wong, I-Jen Chiang

研究成果: 雜誌貢獻文章同行評審

35 引文 斯高帕斯(Scopus)


Midline shift is one of the most important quantitative features clinicians use to evaluate the severity of brain compression by various pathologies. It can be recognized by modeling brain deformation according to the estimated biomechanical properties of the brain and the cerebrospinal fluid spaces. This paper proposes a novel method to identify the deformed midline according to the above hypothesis. In this model, the deformed midline is decomposed into three segments: the upper and the lower straight segments representing parts of the tough dura mater separating two brain hemispheres, and the central curved segment formed by a quadratic Bezier curve, representing the intervening soft brain tissue. The deformed midline is obtained by minimizing the summed square of the differences across all midline pixels, to simulate maximal bilateral symmetry. A genetic algorithm is applied to derive the optimal values of the control points of the Bezier curve. Our algorithm was evaluated on pathological images from 81 consecutive patients treated in a single institute over a period of one year. Our algorithm is able to recognize the deformed midlines in 65 (80%) of the patients with an accuracy of 95%, making it a useful tool for clinical decision-making.

頁(從 - 到)331-339
期刊Computers in Biology and Medicine
出版狀態已發佈 - 3月 2010

ASJC Scopus subject areas

  • 電腦科學應用
  • 健康資訊學


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