Automated boundary segmentation and wound analysis for longitudinal corneal OCT images

Optical coherence tomography (OCT) has been widely applied in the examination and diagnosis of corneal diseases, but the information directly achieved from the OCT images by manual inspection is limited. We propose an automatic processing method to assist ophthalmologists in locating the boundaries in corneal OCT images and analyzing the recovery of corneal wounds after treatment from longitudinal OCT images. It includes the following steps: preprocessing, epithelium and endothelium boundary segmentation and correction, wound detection, corneal boundary fitting and wound analysis. The method was tested on a data set with longitudinal corneal OCT images from 20 subjects. Each subject has five images acquired after corneal operation over a period of time. The segmentation and classification accuracy of the proposed algorithm is high and can be used for analyzing wound recovery after corneal surgery.

[1]  Imran Ahmed,et al.  Automatic Lung Tumor Detection Based on GLCM Features , 2014, ACCV Workshops.

[2]  J. Schuman,et al.  Optical coherence tomography. , 2000, Science.

[3]  David A. Clausi,et al.  Automated 3D Reconstruction and Segmentation from Optical Coherence Tomography , 2010, ECCV.

[4]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[6]  Hossein Rabbani,et al.  Obtaining Thickness Maps of Corneal Layers Using the Optimal Algorithm for Intracorneal Layer Segmentation , 2016, Int. J. Biomed. Imaging.

[7]  Xiaoou Tang,et al.  Texture information in run-length matrices , 1998, IEEE Trans. Image Process..

[8]  R. Sukanesh A Padma,et al.  Automatic Classification and Segmentation of Brain Tumor in CT Images using Optimal Dominant Gray level Run length Texture Features , 2011 .

[9]  David A. Clausi,et al.  A Novel Algorithm for Extraction of the Layers of the Cornea , 2009, 2009 Canadian Conference on Computer and Robot Vision.

[10]  Cigdem Demir,et al.  Graph Run-Length Matrices for Histopathological Image Segmentation , 2011, IEEE Transactions on Medical Imaging.

[11]  Joseph A. Izatt,et al.  Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming , 2011, Biomedical optics express.

[12]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[13]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Shanhui Fan,et al.  Optical coherence tomography for whole eye segment imaging. , 2012, Optics express.