Fast automatic medical image segmentation based on spatial kernel fuzzy c-means on level set method

Fast two-cycle (FTC) model is an efficient and the fastest Level set image segmentation. But, its performance is highly dependent on appropriate manual initialization. This paper proposes a new algorithm by combining a spatially constrained kernel-based fuzzy c-means (SKFCM) algorithm and an FTC model to overcome the mentioned problem. The approach consists of two successive stages. First, the SKFCM makes a rough segmentation to select the initial contour automatically. Then, a fuzzy membership matrix of the region of interest, which is generated by the SKFCM, is used in the next stage to produce an initial contour. Eventually, the FTC scheme segments the image by a curve evolution based on the level set. Moreover, the fuzzy membership degree from the SKFCM is incorporated into the fidelity term of the Chan–Vese model to improve the robustness and accuracy, and it is utilized for the data-dependent speed term of the FTC. A performance evaluation of the proposed algorithm is carried out on the synthetic and real images. The experimental results show that the proposed algorithm has advantages in accuracy, computational time and robustness against noise in comparison with the KFCM, the SKFCM, the hybrid model of the KFCM and the FTC, and five different level set methods on medical image segmentation.