State-of-the-art in retinal optical coherence tomography image analysis

Abstract

Optical coherence tomography (OCT) is an emerging imaging modality that has been widely used in the field of biomedical imaging. In the recent past, it has found uses as a diagnostic tool in dermatology, cardiology, and ophthalmology. In this paper we focus on its applications in the field of ophthalmology and retinal imaging. OCT is able to non-invasively produce cross-sectional volumetric images of the tissues which can be used for analysis of tissue structure and properties. Due to the underlying physics, OCT images suffer from a granular pattern, called speckle noise, which restricts the process of interpretation. This requires specialized noise reduction techniques to eliminate the noise while preserving image details. Another major step in OCT image analysis involves the use of segmentation techniques for distinguishing between different structures, especially in retinal OCT volumes. The outcome of this step is usually thickness maps of different retinal layers which are very useful in study of normal/diseased subjects. Lastly, movements of the tissue under imaging as well as the progression of disease in the tissue affect the quality and the proper interpretation of the acquired images which require the use of different image registration techniques. This paper reviews various techniques that are currently used to process raw image data into a form that can be clearly interpreted by clinicians.

Keywords: Image analysis; image registration; image segmentation; noise reduction; optical coherence tomography (OCT).

Figure 1

Optical coherence tomography of human retina and optic nerve [(Reprinted with permission) (2)].

Figure 2

A typical optical coherence tomography (OCT) system [(Reprinted with permission) (3)].

Figure 3

Typical retinal optical coherence tomography (OCT) image degraded by speckle noise.

Figure 4

Fourier-domain optical coherence tomography (FD-OCT) image of optical nerve head, before (A) and after (B) curvelet coefficients shrinkage-based speckle noise reduction [(Reprinted with permission) (30)].

Figure 5

Results of segmentation for three different retinal optical coherence tomography (OCT) images with method proposed in [(Reprinted with permission) (60)].

Figure 6

Result of the low-rank/sparse decomposition based method proposed in (73).

Figure 7

Color fundus reference image superimposed by the corresponding blood vessel ridge image (A), en face OCT image superimposed by the corresponding blood vessel ridge image (B), the registration result of blood vessel ridge image (C) and intensity images (D) [(Reprinted with permission) (75)].

Figure 8

Optical coherence tomography (OCT) image with blood vessel discontinuity caused by eye movement (A) and corrected image (B) [(Reprinted with permission) (82)].

Figure 9

Wide-field mosaic of retinal layers displaying three main vessel layers. The initial images are on the left and the final result is on the right [(Reprinted with permission) (86)].