Image segmentation, such as to extract an object from a background, is very useful for medical and biological image analysis. In this work, we propose new segmentation methods for interactive segmentation of multidimensional images, based on the Image Foresting Transform (IFT), by exploiting for the first time non-smooth connectivity functions (NSCF) with a strong theoretical background. The new algorithms provide global optimum solutions according to an energy function of graph cut, subject to high-level boundary constraints (polarity and shape). Our experimental results indicate substantial improvements in accuracy in relation to other state-of-the-art methods, using medical images by allowing the customization of the segmentation to a given target object.
Image Segmentation by Image Foresting Transform with Boundary Polarity and Shape Constraints
