Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming

被引：100

作者：

LaRocca, Francesco ^{[1
]}

Chiu, Stephanie J. ^{[1
]}

McNabb, Ryan P. ^{[1
]}

Kuo, Anthony N. ^{[2
]}

Izatt, Joseph A. ^{[1
,2
]}

Farsiu, Sina ^{[1
,2
]}

机构：

[1] Duke Univ, Dept Biomed Engn, Durham, NC 27708 USA

[2] Duke Univ, Med Ctr, Dept Ophthalmol, Durham, NC 27710 USA

来源：

BIOMEDICAL OPTICS EXPRESS | 2011年 / 2卷 / 06期

关键词：

OPTICAL COHERENCE TOMOGRAPHY; HIGH-SPEED; THICKNESS MEASUREMENTS; IN-VIVO; CURVATURE; TENSION;

D O I：

10.1364/BOE.2.001524

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Segmentation of anatomical structures in corneal images is crucial for the diagnosis and study of anterior segment diseases. However, manual segmentation is a time-consuming and subjective process. This paper presents an automatic approach for segmenting corneal layer boundaries in Spectral Domain Optical Coherence Tomography images using graph theory and dynamic programming. Our approach is robust to the low-SNR and different artifact types that can appear in clinical corneal images. We show that our method segments three corneal layer boundaries in normal adult eyes more accurately compared to an expert grader than a second grader-even in the presence of significant imaging outliers. (C)2011 Optical Society of America

引用

收藏

页码：1524 / 1538

页数：15

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