Image Segmentation Using Active Contours Driven by Bias Fitted Image Robust to Intensity Inhomogeneity

In this paper, a novel region-based active contour method is proposed based to both correct and segment the intensity inhomogeneous images. A phase stretch transform (PST) kernel is used to compute new intensity means and bias field, which are employed to define a bias fitted image. In the proposed energy function, a new signed pressure force (SPF) function is formulated with a bias image fitted difference, which helps to segment the intensity inhomogeneous objects. A Gaussian kernel is also used to regularize the level set curve, which also removes the computationally expensive re-initialization. Finally, the proposed method is compared with the state-of-the-art both qualitatively and quantitatively using the synthetic and real brain magnetic resonance (MR) images, which shows it yields the best segmentation and correction results.