@article{Gawish_Haines_Marschall_Wong_Sorbara_Bizheva_Fieguth_2016, title={Improved OCT Human Corneal segmentation Using Bayesian Residual Transform}, volume={2}, url={https://openjournals.uwaterloo.ca/index.php/vsl/article/view/117}, DOI={10.15353/vsnl.v2i1.117}, abstractNote={<p>The inherent poor signal to noise ratio of Optical Coherent Tomography<br />(OCT) is considered as a main limitation of OCT segmentation,<br />particularly because images are sampled quickly, at high resolutions,<br />and in-vivo. Furthermore, speckle noise is generated by<br />the reflections of the OCT LASER limits the ability of automatically<br />segmenting OCT images. This paper presents a novel method to<br />automatically segment human corneal OCT images. The proposed<br />method uses Bayesian Residual Transform (BRT) to build a noise<br />robust external force map, that guides active contours model to the<br />corneal data in OCT images. Experimental results show that the<br />proposed method outperforms the classical as well as the state-ofthe-<br />art methods.</p>}, number={1}, journal={Journal of Computational Vision and Imaging Systems}, author={Gawish, Ahmed and Haines, L. and Marschall, S. and Wong, Alexander and Sorbara, L. and Bizheva, Kostadinka and Fieguth, Paul}, year={2016}, month={Oct.} }