Undecimated hierarchical active contours for oct image segmentation

A limitation of Optical Coherence Tomography (OCT) image segmentation is the poor signal-to-noise ratio of the imaging process, particularly because images are sampled quickly, at high resolutions, and in-vivo. Furthermore, speckle noise is generated by the reflections of the OCT LASER. Because OCT is widely used in imaging the cornea, retina, and skin, OCT layer segmentation is of key interest in all applications. In this paper, a multi-resolution parametric active contour is used for OCT segmentation. The proposed method uses an undecimated wavelet transform to obtain scale-dependent noise reduction, while the active contour is initialized with a generalized Hough transform. Experimental results show that the proposed method outperforms classical as well as state-of-the-art methods and segments OCT images with high level of accuracy.