摘要:
For each of a number of landmarks in an image an initial position of the landmark is defined. Next a neighborhood around the initial position, comprising a number of candidate locations of the landmark is sampled and a cost is associated with each of the candidate locations. A cost function expressing a weighted sum of overall gray level cost and overall shape cost for all candidate locations is optimized. A segmented anatomic entity is defined as a path through a selected combination of candidate locations for which combination the cost function is optimized.
摘要:
For each of a number of landmarks in an image an initial position of the landmark is defined. Next a neighborhood around the initial position, comprising a number of candidate locations of the landmark is sampled and a cost is associated with each of the candidate locations. A cost function expressing a weighted sum of overall gray level cost and overall shape cost for all candidate locations is optimized. A segmented anatomic entity is defined as a path through a selected combination of candidate locations for which combination the cost function is optimized.
摘要:
A gray value model is generated encoding photometric knowledge at landmark positions. This step exploits intensity correlation in neighborhoods sampled around landmark positions. A geometric model is generated encoding geometric knowledge between landmarks. This step exploits spatial correlation between landmarks of segmented anatomic entities.
摘要:
For each of a number of landmarks in an image an initial position of the landmark is defined. Next a neighborhood around the initial position, comprising a number of candidate locations of the landmark is sampled and a cost is associated with each of the candidate locations. A cost function expressing a weighted sum of overall gray level cost and overall shape cost for all candidate locations is optimized. A segmented anatomic entity is defined as a path through a selected combination of candidate locations for which combination the cost function is optimized.
摘要:
An image point in a displayed reference image R is selected and a non-rigid transformation resulting in a transformation field g(rR) mapping every location rR to a corresponding location rF in a floating image F is applied, next a rigid body transformation is applied to floating image F such that rF coincides with the selected image point and the transformed floating image is displayed.
摘要:
An image point in a displayed reference image R is selected and a non-rigid transformation resulting in a transformation field g(rR) mapping every location rR to a corresponding location rF in a floating image F is applied, next a rigid body transformation is applied to floating image F such that rF coincides with the selected image point and the transformed floating image is displayed.
摘要翻译:选择所显示的参考图像R中的图像点,并且导致将每个位置r R i映射到相应位置的变换字段g(r R R)的非刚性变换 应用浮动图像F中的R< F< />接下来,将刚体变换应用于浮动图像F,使得R≠F与所选择的图像点重合,并且变换的浮动图像 被展示。
摘要:
A workflow method for temporal nodule review by registering a reference image R with a floating image F, convolving the reference image R and the floating image with the same window function Hw to generate Rw and Fw, generating a subtraction image by performing subtraction Rw−Fw (g(r)) wherein r represents a voxel (x, y, z) in reference image R, applying a pattern detector to said subtraction image to detect corresponding nodules in reference image R and floating image F and displaying corresponding nodules.
摘要:
A workflow method for temporal nodule review by registering a reference image R with a floating image F, convolving the reference image R and the floating image with the same window function Hw to generate Rw and Fw, generating a subtraction image by performing subtraction Rw−Fw (g(r)) wherein r represents a voxel (x, y, z) in reference image R, applying a pattern detector to said subtraction image to detect corresponding nodules in reference image R and floating image F and displaying corresponding nodules.
摘要翻译:一种用于通过用参考图像R注册浮动图像F的时间结节评估的工作流程方法,将参考图像R和具有相同窗口函数H w w的浮动图像进行卷积以产生R < 通过执行减法R(w(r))生成减法图像,其中r表示 在参考图像R中的体素(x,y,z),将模式检测器应用于所述减影图像,以检测参考图像R和浮动图像F中的相应结节并显示相应的结节。
摘要:
Method to bring out a temporal difference between corresponding structures in a reference image R and a floating image F by convolving the reference image R and the floating image F with a window function Hw to generate Rw and Fw, applying a non-rigid transformation resulting in a transformation field g(rR) mapping every location rR to a corresponding location rF in the floating image F and generating a subtraction image by performing subtraction Rw(r)−Fw(g(r)) wherein r represents a voxel (x, y, z) in reference image R.
摘要:
Method to bring out a temporal difference between corresponding structures in a reference image R and a floating image F by convolving the reference image R and the floating image F with a window function Hw to generate Rw and Fw, applying a non-rigid transformation resulting in a transformation field g(rR) mapping every location rR to a corresponding location rF in the floating image F and generating a subtraction image by performing subtraction Rw(r)−Fw(g(r)) wherein r represents a voxel (x, y, z) in reference image R.
摘要翻译:通过将参考图像R和浮动图像F与窗口函数H 进行卷积来引出参考图像R和浮动图像F中的对应结构之间的时间差的方法, 使用非刚性变换,导致变换字段g(r R R)映射每个位置r SUB >浮动图像F中的相应位置r> F>并且通过执行减法R&gt;(r)-F(W)生成减法图像(&lt; g(r))其中r表示参考图像R中的体素(x,y,z)