A Novel approach to Image Segmentation with Application to MRI
Z. Chaithanya, K. Lokesh Krishna, D. Srihari · 2013
Intensity non-uniformity or intensity in homogeneity usually occurs in Real world Images, those images cannot be segmented by using image segmentation. The most commonly used algorithms in image segmentation are region based and depends on the homogeneity of the image intensities which usually fails to produce accurate segmentation results due to the intensity non-uniformity. In this paper a novel region based method for image segmentation which can be able to discuss with intensity non-uniformities in image segmentation is proposed. First according to the image models with intensity non-uniformities a local clustering criterion function is defined for the intensities in the image neighbourhood of each part. The local clustering criterion function is then integrated with respect to the neighbourhood center to give a global criterion of image segmentation. In a level set formulation this criterion defines an energy in terms of level set functions that represents the partition of image domain and a bias field that corresponds to the intensity non-uniformity of the image. Therefore, by minimizing the energy we can able to segment the image simultaneously and estimate the bias field can be used for the intensity non-uniformity correction. This method is applied on MRI images and real world images of various modalities with desirable performance in the presence of intensity nonuniformities. The experiment results show that the method is stronger, faster and more accurate than the wellknown piecewise smooth model and gives promising results. As an application this method is used for segmentation and bias correction of real world images and MRI images with better results.