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 Multi-Scale Hessian-Based Measure 參數設定

 Multi-Scale Hessian-Based Measure 參數設定. Wei –Lu Lin. Outline. MultiScaleHessianBasedMeasureImageFilter H essianToObjectnessMeasureImageFilter Example( Vessels case :IM065.dcm). I tk :: MultiScaleHessianBasedMeasureImageFilter.

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 Multi-Scale Hessian-Based Measure 參數設定

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  1.  Multi-Scale Hessian-Based Measure參數設定 Wei –Lu Lin

  2. Outline MultiScaleHessianBasedMeasureImageFilter HessianToObjectnessMeasureImageFilter Example(Vessels case :IM065.dcm)

  3. Itk:: MultiScaleHessianBasedMeasureImageFilter • A filter to enhance structures using Hessian eigensystem-based measures in a multiscale framework • SetSigmaMinimum • Set/Get macros for SigmaMin • SetSigmaMaximum • Set/Get macros for SigmaMax • SetNumberOfSigmaSteps • Set/Get macros for Number of Scales

  4. Itk::HessianToObjectnessMeasureImageFilter • A filter to enhance M-dimensional objects in N-dimensional images • SetAlpha • SetBeta • SetGamma

  5. Itk::HessianToObjectnessMeasureImageFilter • SetAlpha • Set/Get Alpha, the weight corresponding to R_A (the ratio of the smallest eigenvalue that has to be large to the larger ones). Smaller values lead to increased sensitivity to the object dimensionality

  6. Itk::HessianToObjectnessMeasureImageFilter • SetBeta • Set/Get Beta, the weight corresponding to R_B (the ratio of the largest eigenvalue that has to be small to the larger ones). Smaller values lead to increased sensitivity to the object dimensionality

  7. Itk::HessianToObjectnessMeasureImageFilter • SetGamma • Set/Get Gamma, the weight corresponding to S (the Frobenius norm of the Hessian matrix, or second-order structureness)

  8. Example sigmaMinimum = 2.0 sigmaMaximum = 3.0 numberOfSigmaSteps = 1 Alpha = 0.5 Beta = 0.5 Gamma = 5.0 Vessel case :IM065.dcm

  9. Example Tune the sigmaMinimum and sigmaMaximum sigmaMinimum = 10.0 sigmaMaximum = 11.0 sigmaMinimum = 2.0 sigmaMaximum = 3.0

  10. Example Tune the numberOfSigmaSteps numberOfSigmaSteps = 10 numberOfSigmaSteps = 1

  11. Example Tune the Alpha Alpha = 0.5 Alpha = 10.0

  12. Example Tune the Beta Beta = 0.5 Beta = 10.0

  13. Example • Tune the Gamma Gamma = 2.0 Gamma = 5.0

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