Retinal Nerve Fiber Layer Segmentation of Spectral Domain Optical Coherence Tomography Images Based on Random Forest

CHEN Qiang; XU Jun; NIU Sijie

doi:10.11999/JEIT160663

Volume 39 Issue 5

May 2017

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CHEN Qiang, XU Jun, NIU Sijie. Retinal Nerve Fiber Layer Segmentation of Spectral Domain Optical Coherence Tomography Images Based on Random Forest[J]. Journal of Electronics & Information Technology, 2017, 39(5): 1101-1108. doi: 10.11999/JEIT160663

Citation:

CHEN Qiang, XU Jun, NIU Sijie. Retinal Nerve Fiber Layer Segmentation of Spectral Domain Optical Coherence Tomography Images Based on Random Forest[J]. Journal of Electronics & Information Technology, 2017, 39(5): 1101-1108. doi: 10.11999/JEIT160663

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Retinal Nerve Fiber Layer Segmentation of Spectral Domain Optical Coherence Tomography Images Based on Random Forest

doi: 10.11999/JEIT160663

1.
2.
(School of Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China)

Funds:

The National Natural Science Foundation of China (61671242), The Special Funds of Fundamental Research for the Central Universities (30920140111004), Six Big Talent Peals (2014-SWYY-024), The Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University)(MJUKF201706)

Received Date: 2016-06-24
Rev Recd Date: 2017-03-23
Publish Date: 2017-05-19

Abstract

Abstract

Spectral Domain Optical Coherence Tomography (SD-OCT) imaging technique is widely used in the diagnosis of ophthalmology diseases. The segmentation of retinal layers plays a very important role in the diagnosis of glaucoma. In this paper, a random forest classifier is used which is trained by twelve different features to find the boundaries between layers. Whats more, the relative gray feature and the neighbor features are used to solve the problem of large errors under the condition of uneven illumination. In the last, the segmentation results of the proposed algorithm, a traditional algorithm and Iowa segmentation software on ten sets of retinal images are compared with manual segmentation, and the average absolute boundary errors are 9.202.57m, 11.332.99m, 10.273.01m, respectively. The experiments show that the proposed algorithm can segment the Retinal Never Fiber Layer (RNFL) better.
- Spectral Domain Optical Coherence Tomography (SD-OCT),
- Glaucoma,
- Retinal image segmentation,
- Retinal Never Fiber Layer (RNFL),
- Random forest

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References(14)

References

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