Medical Application of Image Segmentation with Intensity Inhomogeneities

Most image segmentation techniques are based on the intensity homogeneity. Intensity inhomogeneity frequently occurs in real word image like MRI, ultrasound, satellite images etc.This type of images fail to provide accurate segmentation result; this is challenging issue. In this paper propose a novel region based method for image segmentation, which is help to deal with intensity inhomogeneity images. First based on a model of images with intensity inhomogeneities, we derive a local intensity clustering property of the image intensities and define a local clustering criterion function for the images intensities in a neighborhood of each point. This local clustering criterion is then integrated with respect to the neighborhood center to give global criterion of image segmentation. In a level set formulation, this criterion defines an energy in terms of level set function that represent a partition of the image domain and bias field that accounts for the intensity inhomogeneity of the image. Therefore by minimizing this energy, our method is able to simultaneously segment the image and estimate he bias field, and the estimated bias field can be used for intensity inhomogeneity correction (or bias correction).