Midline shift (MLS) is one of the most important quantitative features clinicians use to evaluate the severity of brain compression. It can be recognized by modeling brain deformation according to the estimated biomechanical properties of the brain structures. This paper proposes a novel method to identify the deformed midline by decomposing it into three segments: the upper and the lower straight segments representing parts of the tough meninges separating two brain hemispheres, and the central curved segment formed by a quadratic Bézier curve, representing the intervening soft brain tissue. The deformed midline is obtained by minimizing the summed square of the differences across all midline points, applying a genetic algorithm. Our algorithm was evaluated on images containing various pathologies from 81 consecutive patients treated in a single institute over one-year period. The deformed midlines were evaluated by human experts, and the values of midline shift were accurate in 95%.
|Proceedings - IEEE International Conference on Data Mining, ICDM
|已發佈 - 2006
|6th IEEE International Conference on Data Mining - Workshops, ICDM 2006 - Hong Kong, 中国
持續時間: 12月 18 2006 → 12月 18 2006
|6th IEEE International Conference on Data Mining - Workshops, ICDM 2006
|12/18/06 → 12/18/06
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