3D OCT retinal vessel segmentation based on boosting learning

摘要

Blood vessel on retina is generally used for medical image registration. Three dimensional (3D) OCT is the new technique capable of providing the detailed 3D structure of retina. Most algorithms of 3D OCT vessel segmentation need to use the result of retinal layer segmentation to enhance vessel Bittern. The proposed 3D boosting learning algorithm is an independent pixel (A-scan projection on OCT fundus image) classification algorithm, which does not rely on any processing result Both 2D features from OCT fundus image and the third dimensional Haar-feature generated from each A-scan are used in the boosting learning. A matched template, second-order Gaussian filter is used to post-process the generated binary vessel image to clean up the false classifications and flnooth the vessels. Eleven images were tested and compared with the manually marked reference. The average sensitivity and specificity were 85% and 88% respectively. The proposed algorithm is an efficient way to automatically identify the blood vessel on 3D OCT image without the need of pre-legmentation.