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A hierarchical method based on active shape models and directed Hough transform for segmentation of noisy biomedical images; application in segmentation of pelvic X-ray images

机译:一种基于主动形状模型和定向霍夫变换的分层方法用于分割嘈杂的生物医学图像;在骨盆X线图像分割中的应用

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摘要

BackgroundTraumatic pelvic injuries are often associated with severe, life-threatening hemorrhage, and immediate medical treatment is therefore vital. However, patient prognosis depends heavily on the type, location and severity of the bone fracture, and the complexity of the pelvic structure presents diagnostic challenges. Automated fracture detection from initial patient X-ray images can assist physicians in rapid diagnosis and treatment, and a first and crucial step of such a method is to segment key bone structures within the pelvis; these structures can then be analyzed for specific fracture characteristics. Active Shape Model has been applied for this task in other bone structures but requires manual initialization by the user. This paper describes a algorithm for automatic initialization and segmentation of key pelvic structures - the iliac crests, pelvic ring, left and right pubis and femurs - using a hierarchical approach that combines directed Hough transform and Active Shape Models.
机译:背景技术骨盆外伤常伴有严重的危及生命的出血,因此立即就医至关重要。但是,患者的预后在很大程度上取决于骨折的类型,位置和严重程度,而骨盆结构的复杂性给诊断带来了挑战。从最初的患者X射线图像中自动检测骨折可以帮助医生快速诊断和治疗,这种方法的第一步也是至关重要的一步是分割骨盆内的关键骨结构。然后可以分析这些结构的特定断裂特征。活动形状模型已在其他骨骼结构中用于此任务,但需要用户手动初始化。本文介绍了一种使用结合了定向霍夫变换和主动形状模型的分层方法来自动初始化和分割骨盆关键结构(c,骨盆环,左右耻骨和股骨)的算法。

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