Tumor in brain Magnetic Resonance Imaging (MRI) images is often difficult to be segmented accurately due to noise, gray inhomogeneity, complex structrue, fuzzy and discontinuous boundaries. For the purpose of getting precise segmentation with less position bias, a new method based on Fuzzy C-Means (FCM) clustering and morphological multi-scale modification was proposed. Firstly, a control parameter was introduced to distinguish noise points, edge points and regional interior points in neighborhood, and the function relationship between pixels and the sizes of structure elements was established by combining with spatial information. Then, different pixels were modified with different-sized structure elements using morphological closing operation. Thus most local minimums caused by irregular details and noises were removed, while region contours positions corresponding to the target area were largely unchanged. Finally, FCM clustering algorithm was employed to implement segmentation on the basis of multi-scale modified image, which avoids the local optimization, misclassification and region contours position bias, while remaining accurate positioning of contour area. Compared with the standard FCM, Kernel FCM (KFCM), Genetic FCM (GFCM), Fuzzy Local Information C-Means (FLICM) and expert hand sketch, the experimental results show that the suggested method can achieve more accurate segmentation result, owing to its lower over-segmentation and under-segmentation, as well as higher similarity index compared with the standard segmentation.
