An automated framework of inner segment/outer segment defect detection for retinal SD-OCT images
Abstract
The integrity of inner segment/outer segment (IS/OS) has high correlation with lower visual acuity in patients suffering from blunt trauma. An automated 3D IS/OS defect detection method based on the SD-OCT images was proposed. First, 11 surfaces were automatically segmented using the multiscale 3D graph-search approach. Second, the sub-volumes between surface 7 and 8 containing IS/OS region around the fovea (diameter of mm) were extracted and flattened based on the segmented retinal pigment epithelium layer. Third, 5 kinds of texture based features were extracted for each voxel. A KNN classifier was trained and each voxel was classified as disrupted or nondisrupted and the responding defect volume was calculated. The proposed method was trained and tested on 9 eyes from 9 trauma subjects using the leave-one-out cross validation method. The preliminary results demonstrated the feasibility and efficiency of the proposed method.
How to Cite:
Zhu, W. & Shi, F. & Xiang, D. & Gao, E. & Wang, L. & Chen, H. & Chen, X., (2014) “An automated framework of inner segment/outer segment defect detection for retinal SD-OCT images”, Proceedings of the Ophthalmic Medical Image Analysis International Workshop 1(2014), 49-56. doi: https://doi.org/10.17077/omia.1008
Rights: Copyright © 2014, Weifang Zhu, Fei Shi, Dehui Xiang, Enting Gao, Liyun Wang, Haoyun Chen, and Xinjian Chen.
Downloads:
Download pdf
View
PDF