A local modified chanvese model for segmenting inhomogeneous multiphase images
In this article, we propose a novel model to overcome the drawbacks of the modified ChanVese (MCV) model. Our model is devoted to find an optimal partition of inhomogeneous regions accurately and computationally efficient. MCV model was proposed on the concept of using one level-set function for one region. It needs fewer numbers of iterations and improves the efficiency of image segmentation in contrast to the multiphase ChanVese model. The MCV model, however, is highly dependent on initial curves placement and often leads to erroneous segmentations on images with intensity inhomogeneity. In our model, to eliminate the affection of background information on the curve evolution and speed up the curve evolution, we first use the k-means algorithm to presegment the image to get the initial curves and then add the local image information to the total energy functional of MCV model to deal with the intensity inhomogeneity. Finally, extensive experiments are done and the segmentation results on homogeneous multiphase images verify that the proposed method has the better accuracy and efficiency comparing to MCV model. Moreover, we show results on challenging multiphase inhomogeneous image to illustrate the robust and accurate segmentation that are possible with this novel model.  2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 103113, 2012
chanvese model intensity inhomogeneity k-means level set