Biomedical images are usually corrupted by strong noise and
intensity inhomogeneity simultaneously. Existing regionbased active contour models (RACMs) easily fail when segmenting such images. In the frequency domain, we propose a
generalized RACM that presents a new way to understand the
essence of classical RACMs whose segmentation results are
determined by a frequency filter to extract the proposed frequency boundary energy. Then, we introduce the difference
of Gaussians as the optimal filter to exclude strong noise and
intensity inhomogeneity effectively. We show superior performance of the model by comparing with six state-of-the-art
methods on challenge biomedical images and segmenting an
optical coherence tomography image sequence.