Computational Methods and Clinical Applications for Spine Imaging

Computational Methods and Clinical Applications for Spine Imaging
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脊柱成像的计算方法和临床应用

DOI:
10.1007/978-3-319-55050-3_1
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发表时间:
2016
期刊:
--
影响因子:
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通讯作者:
Al Arif S
Al Arif S
中科院分区:
--
文献类型:
--
作者:
Al Arif S

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X光是诊断颈椎损伤的一种常见方法。急诊医生漏掉了许多伤害,这些伤害后来导致了危及生命的并发症。计算机辅助分析X射线图像有可能发现遗漏的伤害。椎骨分割是实现自动损伤检测系统的关键一步。主动形状模型(ASM)是目前最成功、最流行的脊椎分割方法之一。在这项工作中,我们提出了一种新的基于随机分类森林的ASM搜索方法和基于核密度估计的预测技术。该方法已经在包含450个椎骨的90个急诊室X光图像的数据集上进行了测试,其性能优于经典的基于马氏距离的ASM搜索方法和基于回归森林的方法。
X-ray is a common modality for diagnosing cervical vertebrae injuries. Many injuries are missed by emergency physicians which later causes life threatening complications. Computer aided analysis of X-ray images has the potential to detect missed injuries. Segmentation of the vertebrae is a crucial step towards automatic injury detection system. Active shape model (ASM) is one of the most successful and popular method for vertebrae segmentation. In this work, we propose a new ASM search method based on random classification forest and a kernel density estimation-based prediction technique. The proposed method have been tested on a dataset of 90 emergency room X-ray images containing 450 vertebrae and outperformed the classical Mahalanobis distance-based ASM search and also the regression forest-based method.
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