Modified Data Fidelity Speed in Anisotropic Diffusion

In this paper, we use an anisotropic diffusion in a level set framework for low-level segmentation of necrotic femoral heads. Our segmentation is based on three speed terms. The first one includes an adaptive estimation of the contrast level. We use the entropy for evaluating our diffusion on synthetic 3D data. We notice that using the data fidelity term in the last iterations excessively penalizes the diffusion process. To provide better segmentation results, we propose some modifications in the data fidelity speed: we propose to build its reference data term from previous iterations results and hence lessening influence of initial noisy data.

[1]  Kaleem Siddiqi,et al.  Geometric heat equation and nonlinear diffusion of shapes and images , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Hui Zhang,et al.  An entropy-based objective evaluation method for image segmentation , 2003, IS&T/SPIE Electronic Imaging.

[3]  Guillermo Sapiro,et al.  Robust anisotropic diffusion , 1998, IEEE Trans. Image Process..

[4]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Martin Rumpf,et al.  A Level Set Method for Anisotropic Geometric Diffusion in 3D Image Processing , 2002, SIAM J. Appl. Math..

[6]  Hideki Yoshikawa,et al.  Automated segmentation of necrotic femoral head from 3D MR data. , 2004, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[7]  P. Lions,et al.  Image selective smoothing and edge detection by nonlinear diffusion. II , 1992 .