A strain energy filter for 3D vessel enhancement with application to pulmonary CT images.
The traditional Hessian-related vessel filters often suffer from detecting complex structures like bifurcations due to an over-simplified cylindrical model. To solve this problem, we present a shape-tuned strain energy density function to measure vessel likelihood in 3D medical images. This method is initially inspired by established stress-strain principles in mechanics. By considering the Hessian matrix as a stress tensor, the three invariants from orthogonal tensor decomposition are used independently or combined to formulate distinctive functions for vascular shape discrimination, brightness contrast and structure strength measuring. Moreover, a mathematical description of Hessian eigenvalues for general vessel shapes is obtained, based on an intensity continuity assumption, and a relative Hessian strength term is presented to ensure the dominance of second-order derivatives as well as suppress undesired step-edges. Finally, we adopt the multi-scale scheme to find an optimal solution through scale space. The proposed method is validated in experiments with a digital phantom and non-contrast-enhanced pulmonary CT data. It is shown that our model performed more effectively in enhancing vessel bifurcations and preserving details, compared to three existing filters.
Division of Image Processing, Department of Radiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands. xcy19722@hotmail.com
Changyan
C
Xiao
Marius
M
Staring
Denis
D
Shamonin
Johan H C
JH
Reiber
Jan
J
Stolk
Berend C
BC
Stoel
D000465:Algorithms; D000792:Angiography / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008433:Mathematics; D019047:Phantoms, Imaging; D016012:Poisson Distribution; D011651:Pulmonary Artery / Q000000981:diagnostic imaging*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*