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    • 9. 发明授权
    • System, method and apparatus for small pulmonary nodule computer aided diagnosis from computed tomography scans
    • 用于计算机断层扫描的小型肺结节计算机辅助诊断系统,方法和装置
    • US07751607B2
    • 2010-07-06
    • US12277877
    • 2008-11-25
    • Anthony P. ReevesDavid YankelevitzClaudia HenschkeAntoni Chan
    • Anthony P. ReevesDavid YankelevitzClaudia HenschkeAntoni Chan
    • G06K9/00
    • G06T7/0012G06T3/0075G06T7/44G06T2207/10081G06T2207/30061
    • The present invention is a multi-stage detection algorithm using a successive nodule candidate refinement approach. The detection algorithm involves four major steps. First, the lung region is segmented from a whole lung CT scan. This is followed by a hypothesis generation stage in which nodule candidate locations are identified from the lung region. In the third stage, nodule candidate sub-images pass through a streaking artifact removal process. The nodule candidates are then successively refined using a sequence of filters of increasing complexity. A first filter uses attachment area information to remove vessels and large vessel bifurcation points from the nodule candidate list. A second filter removes small bifurcation points. The invention also improves the consistency of nodule segmentations. This invention uses rigid-body registration, histogram-matching, and a rule-based adjustment system to remove missegmented voxels between two segmentations of the same nodule at different times.
    • 本发明是使用连续结节候选细化方法的多级检测算法。 检测算法涉及四个主要步骤。 首先,从全肺CT扫描分割肺部区域。 随后是从肺区识别结节候选位置的假设生成阶段。 在第三阶段,结节候选子图像通过条纹伪影去除过程。 然后使用逐渐增加的复杂度的一系列滤波器连续地细化结节候选物。 第一个过滤器使用附件区域信息从结节候选列表中移除血管和大血管分叉点。 第二个过滤器移除小分叉点。 本发明还提高了结节分离的一致性。 本发明使用刚体登记,直方图匹配和基于规则的调整系统,以在不同时间去除相同结节的两个分段之间的错误分割的体素。
    • 10. 发明申请
    • System, Method and Apparatus for Small Pulmonary Nodule Computer Aided Diagnosis from Computed Tomography Scans
    • 系统,方法和装置的小型肺结节计算机辅助诊断从计算机断层扫描
    • US20090080748A1
    • 2009-03-26
    • US12277877
    • 2008-11-25
    • Anthony P. ReevesDavid YankelevitzClaudia HenschkeAntoni Chan
    • Anthony P. ReevesDavid YankelevitzClaudia HenschkeAntoni Chan
    • G06K9/00
    • G06T7/0012G06T3/0075G06T7/44G06T2207/10081G06T2207/30061
    • The present invention is a multi-stage detection algorithm using a successive nodule candidate refinement approach. The detection algorithm involves four major steps. First, the lung region is segmented from a whole lung CT scan. This is followed by a hypothesis generation stage in which nodule candidate locations are identified from the lung region. In the third stage, nodule candidate sub-images pass through a streaking artifact removal process. The nodule candidates are then successively refined using a sequence of filters of increasing complexity. A first filter uses attachment area information to remove vessels and large vessel bifurcation points from the nodule candidate list. A second filter removes small bifurcation points. The invention also improves the consistency of nodule segmentations. This invention uses rigid-body registration, histogram-matching, and a rule-based adjustment system to remove missegmented voxels between two segmentations of the same nodule at different times.
    • 本发明是使用连续结节候选细化方法的多级检测算法。 检测算法涉及四个主要步骤。 首先,从全肺CT扫描分割肺部区域。 随后是从肺区识别结节候选位置的假设生成阶段。 在第三阶段,结节候选子图像通过条纹伪影去除过程。 然后使用逐渐增加的复杂度的一系列滤波器连续地细化结节候选物。 第一个过滤器使用附件区域信息从结节候选列表中移除血管和大血管分叉点。 第二个过滤器移除小分叉点。 本发明还提高了结节分离的一致性。 本发明使用刚体登记,直方图匹配和基于规则的调整系统,以在不同时间去除相同结节的两个分段之间的错误分割的体素。