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.