Segmentation of computer tomography image using local robust statistics and region-scalable fitting

Intensity inhomogeneity may cause considerable difficulties in segmentation of CT image. In order to overcome the difficulties caused by intensity inhomogeneity, the region-scalable fitting (RSF) model was put forward. RSF model draws upon intensity information in local regions with a controllable scale. But only using intensity information may lead to slow convergence rate and poor denoise ability. Combining the method of robust statistics, RSF model is improved in this paper. In the improved model, the intensity in RSF model is replaced with local robust statistics which is the weighted combination of inter-quartile range, mean absolute deviation and intensity median in local region. Inter-quartile range and mean absolute deviation in local region are introduced to sharpen object boundaries, and intensity median in local region is introduced to reduce image noise. The contrast experiments between RSF model and the improved model are provided, which demonstrate the fast convergence rate and robustness to noise of the improved model.