A survey on computer aided diagnosis for ocular diseases

Abstract

Background: Computer Aided Diagnosis (CAD), which can automate the detection process for ocular diseases, has attracted extensive attention from clinicians and researchers alike. It not only alleviates the burden on the clinicians by providing objective opinion with valuable insights, but also offers early detection and easy access for patients.

Method: We review ocular CAD methodologies for various data types. For each data type, we investigate the databases and the algorithms to detect different ocular diseases. Their advantages and shortcomings are analyzed and discussed.

Result: We have studied three types of data (i.e., clinical, genetic and imaging) that have been commonly used in existing methods for CAD. The recent developments in methods used in CAD of ocular diseases (such as Diabetic Retinopathy, Glaucoma, Age-related Macular Degeneration and Pathological Myopia) are investigated and summarized comprehensively.

Conclusion: While CAD for ocular diseases has shown considerable progress over the past years, the clinical importance of fully automatic CAD systems which are able to embed clinical knowledge and integrate heterogeneous data sources still show great potential for future breakthrough.

Figure 1

Ocular Anatomy and various image modalities. An illustration of the parts of the eye and the imaging modalities associated with them.

Figure 2

Publication trends for ocular disease detection. (a) Number of publications each year for different ocular imaging modality (b) Number of publications each year for different ocular disease detection using retinal image (queries to IEEEXplore are as on May 2013).

Figure 3

How does DR look in a DFP. (a) DFP of a normal eye. (b) DFP of an eye affected with DR. (c) Common lesions associated with DR. (d) A distribution showing number of works detecting each type of symptom.

Figure 4

Major structures of the optic disc in DFP. The region enclosed by the blue line is the optic disc; the central bright zone enclosed by the red line is the optic cup; and the region between the red and blue lines is the neuroretinal rim.

Figure 5

Vision damage caused by AMD. (a) Image of a normal eye. (b) Image of an eye affected with AMD. (Image taken from Wikipedia http://en.wikipedia.org/wiki/Macular_degeneration).

Figure 6

Heritability for various ocular traits. The range of heritability values for different ocular traits. A higher heritability value means a higher change of inheriting the trait.

Figure 7

Ocular disease related SNPs found in OMIM and GWAS Catalogue. (query made on May 8th, 2013).

Figure 8

Cross-sectional images of the spectral-domain OCT volume in glaucoma. (a) X-Y image of the OCT volume. (b) X-Z image of the OCT volume corresponding to the horizontal line in (a). (c) Y-Z image of the OCT volume corresponding to the vertical line in (a).

Figure 9

Example images of the central retina. Optic nerve head (ONH) centred fundus photograph (a) is used for automated glaucoma detection by the proposed glaucoma risk index while glaucoma probability score utilizes HRT 2.5-dimensional topography images (b) Images taken from [90].

Figure 10

Images generated by the GDx VCC. (a) The reflectance image, which is displayed as a colored intensity map (greater reflectance corresponds to a lighter color). (b) The retardation map converted to RNFL thickness. The RNFL thickness is color-coded based on the color spectrum with thinner regions displayed in blue and green and thicker regions displayed in yellow and red [281].

Figure 11

The symptoms of AMD seen in DFP. (a) DFP of a healthy eye. (b) DFP of an eye affected with dry AMD, with drusen presented. (c) DFP of an eye affected with wet AMD. Presence of exudates can be seen.

Figure 12

Example of AMD related exudate in OCT image. (a) OCT image showing an eye with severe exudate. (b) OCT image showing an eye with medium exudate. (c) OCT image showing a normal eye.

Figure 13

Parts of the right eye as viewed in a slit-lamp image [50]. (a) Important parts of the eye as seen in a slit lamp image. (b) Slit lamp image of a normal eye. (c) Slit lamp image of an eye affected with Nuclear Cataract.

Figure 14

Examples of retroillumination images. Retroillumination images corresponding to (a) a normal eye lens, (b) lens with 61.07% of cortical cataract, and (c) lens with 4.95% of cortical opacities and 31.28% of PSC opacities. Top row shows anterior images while the bottom row shows posterior images [58].