Split Bregman method based level set formulations for segmentation and correction with application to MR images and color images

At present, magnetic resonance (MR) images have gradually become a major aid for clinical medicine, which has greatly improved the doctor's diagnosis rate. Accurate and fast segmentation of MR images plays an extremely important role in medical research. However, due to the influence of external factors and the defects of imaging devices, the MR images have severe intensity inhomogeneity, which poses a great challenge to accurately segment MR images. To deal with this problem, this paper presents an improved active contour model by combining the level set evolution model (LSE) and the split Bregman method, and gives the two-phase, the multi-phase and the vector-valued formulations of our model, respectively. The use of the split Bregman method accelerates the minimization process of our model by reducing the computation time and iterative times. A slowly varying bias field is added into the energy functional, which is the key to correct inhomogeneous images. By estimating the bias fields, not only can we get accurate image segmentation results, but also a homogeneous image after correction is provided. Then we apply our model to segment a large amount of synthetic and real MR images, including gray and color images. Experimental results show that our model can provide satisfactory segmentation and correction results for both gray and color images. Besides, compared with the LSE model, our model has higher accuracy and is superior to the LSE model. In addition, experimental results also demonstrate that our model has the advantages of being insensitive to initial contours and robust to noises.