A multi-scale level set method based on local features for segmentation of images with intensity inhomogeneity

Images with intensity inhomogeneity pose significant challenges in image segmentation. Local region-based level set models have recently been recognized as promising methods to segment such images. In these models, local intensity information in a neighborhood of predetermined size is extracted and then embedded into the energy functional, guiding the evolution of deformable contour toward desired boundaries. The local neighborhood intensities are assumed to be rather constant; therefore, the selection of neighborhood size greatly influences effectiveness and robustness. Complex image characteristics, such as variation in degree of intensity inhomogeneity and noise levels among regions, can lead to severe challenges for accurate image segmentation when using only a fixed scale parameter for local regions. We propose a new multi-scale local feature-based level set method for image segmentation with an improved strategy based on previous studies of multi-scale image filtering methods, which allow for automatic selection of filtering scale parameters. Our novel method can adaptively determine the optimal scale parameter for each pixel during contour evolution, alleviating the challenges caused by severe intensity inhomogeneity. First, we define a Local Maximum Description Difference feature (LMDD), based on multi-scale local region descriptors. We incorporate the LMDD, associated with the maximum response of multi-scale high-pass filters for each pixel, into three local region based level set models with Chan-Vese (CV)-like structure to construct the energy functional. Finally, we complete the segmentation through minimization of this energy. Our experimental results illustrate the good performance of the proposed level set method for segmenting images with severe intensity inhomogeneity.