The role of scanning laser polarimetry using the GDx variable corneal compensator in the management of glaucoma suspects

Normalization of Retinal Birefringence Scanning Signals

Signal amplitudes obtained from retinal scanning depend on numerous factors. Working with polarized light to interrogate the retina, large parts of which are birefringent, is even more prone to artifacts. This article demonstrates the necessity of using normalization when working with retinal birefringence scanning signals in polarization-sensitive ophthalmic instruments. After discussing the pros and cons of employing a normalization signal obtained by means of added optoelectronic hardware, the study shifts over and focuses on a numerical normalization method based on merely the s- and p-polarization components without additional optical or electronic hardware. This minimizes the adverse effects of optical asymmetries, the presence of certain instrumental noise, device-to-device variability, pupil diameter, retinal reflectivity, subject-to-subject variations, the position of the eye in the exit pupil of the device, and even signal degradation by cataracts. Results were experimentally and numerically tested on human data from 15 test subjects and clearly demonstrated the signal standardization achieved by numerical normalization. This is expected to lead to substantial improvement in algorithms and decision-making software, especially in ophthalmic screening instruments for pediatric applications, without added hardware cost. The proposed normalization method is also applicable to other polarization-sensitive optical instruments.

Computer-aided diagnosis of glaucoma using fundus images: A review

BACKGROUND AND OBJECTIVES

Glaucoma is an eye condition which leads to permanent blindness when the disease progresses to an advanced stage. It occurs due to inappropriate intraocular pressure within the eye, resulting in damage to the optic nerve. Glaucoma does not exhibit any symptoms in its nascent stage and thus, it is important to diagnose early to prevent blindness. Fundus photography is widely used by ophthalmologists to assist in diagnosis of glaucoma and is cost-effective.

METHODS

The morphological features of the disc that is characteristic of glaucoma are clearly seen in the fundus images. However, manual inspection of the acquired fundus images may be prone to inter-observer variation. Therefore, a computer-aided detection (CAD) system is proposed to make an accurate, reliable and fast diagnosis of glaucoma based on the optic nerve features of fundus imaging. In this paper, we reviewed existing techniques to automatically diagnose glaucoma.

RESULTS

The use of CAD is very effective in the diagnosis of glaucoma and can assist the clinicians to alleviate their workload significantly. We have also discussed the advantages of employing state-of-art techniques, including deep learning (DL), when developing the automated system. The DL methods are effective in glaucoma diagnosis.

CONCLUSIONS

Novel DL algorithms with big data availability are required to develop a reliable CAD system. Such techniques can be employed to diagnose other eye diseases accurately.

A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images

Glaucoma is an optic neuropathy defined by characteristic damage to the optic nerve and accompanying visual field deficits. Early diagnosis and treatment are critical to prevent irreversible vision loss and ultimate blindness. Current techniques for computer-aided analysis of the optic nerve and retinal nerve fiber layer (RNFL) are expensive and require keen interpretation by trained specialists. Hence, an automated system is highly desirable for a cost-effective and accurate screening for the diagnosis of glaucoma. This paper presents a new methodology and a computerized diagnostic system. Adaptive histogram equalization is used to convert color images to grayscale images followed by convolution of these images with Leung-Malik (LM), Schmid (S), and maximum response (MR4 and MR8) filter banks. The basic microstructures in typical images are called textons. The convolution process produces textons. Local configuration pattern (LCP) features are extracted from these textons. The significant features are selected using a sequential floating forward search (SFFS) method and ranked using the statistical t-test. Finally, various classifiers are used for classification of images into normal and glaucomatous classes. A high classification accuracy of 95.8% is achieved using six features obtained from the LM filter bank and the k-nearest neighbor (kNN) classifier. A glaucoma integrative index (GRI) is also formulated to obtain a reliable and effective system.

Scanning laser polarimetry in glaucoma

Glaucoma is an acquired progressive optic neuropathy which is characterized by changes in the optic nerve head and retinal nerve fiber layer (RNFL). White-on-white perimetry is the gold standard for the diagnosis of glaucoma. However, it can detect defects in the visual field only after the loss of as many as 40% of the ganglion cells. Hence, the measurement of RNFL thickness has come up. Optical coherence tomography and scanning laser polarimetry (SLP) are the techniques that utilize the evaluation of RNFL for the evaluation of glaucoma. SLP provides RNFL thickness measurements based upon the birefringence of the retinal ganglion cell axons. We have reviewed the published literature on the use of SLP in glaucoma. This review elucidates the technological principles, recent developments and the role of SLP in the diagnosis and monitoring of glaucomatous optic neuropathy, in the light of scientific evidence so far.

Symmetry between the right and left eyes of the normal retinal nerve fiber layer measured with optical coherence tomography (an AOS thesis)

PURPOSE

To determine the limits of the normal amount of interocular symmetry in retinal nerve fiber layer (RNFL) thickness measurements obtained with third-generation time domain optical coherence tomography (OCT3).

METHODS

Both eyes of normal volunteers were scanned using the peripapillary standard and fast RNFL algorithms of OCT3.

RESULTS

A total of 108 volunteers were included in the analysis. The mean +/- standard deviation (SD) of age of the volunteers was 46.0 +/- 15.0 years (range 20-82). Forty-two participants (39%) were male and 66 (61%) were female. Mean RNFL thickness correlated extremely well, with intraclass correlation coefficients of 0.89 for both algorithms (95% confidence interval [CI], 0.84-0.93). The mean RNFL thickness of the right eye measured 1.3 mum thicker than the left on the standard scan (SD 4.7, 95% CI 0.4-2.2, P = .004) and 1.2 mum on the fast scan (SD 5.2, 95% CI 0.1-2.2, P = .026). The 95% tolerance limits on the difference between the mean RNFL thicknesses of right minus left eye was -10.8 and +8.9 mum with the standard scan algorithm and -10.6 and +11.7 mum with the fast scan algorithm.

CONCLUSIONS

Mean RNFL thickness between the 2 eyes of normal individuals should not differ by more than approximately 9 to 12 mum, depending on which scanning algorithm of OCT3 is used and which eye measures thicker. Differences beyond this level suggest statistically abnormal asymmetry, which may represent early glaucomatous optic neuropathy.

Diagnostic value of GDx polarimetry in a Taiwan Chinese population

PURPOSE

To study the diagnostic ability of scanning laser polarimetry with variable corneal compensation (GDx VCC) for early glaucomatous (EG) eyes and glaucoma-suspect (GS) eyes in a Taiwan Chinese population.

METHODS

This prospective cross-sectional study included 82 EG eyes (mean deviation, MD: -3.32 +/- 2.20 dB), 45 GS eyes (MD: -2.43 +/- 2.16 dB), and 62 normal eyes. Retinal nerve fiber layer thickness of each subject was measured using GDx VCC and Humphrey Field Analyzer visual field testing. Measured GDx VCC parameters were compared among groups. The area under the receiver operating characteristic (AROC) curve of each parameter was used to differentiate normal from EG eyes or GS eyes. The correlation between MD and each parameter was also evaluated.

RESULTS

For both normal versus EG and normal versus GS, the largest AROC values were for nerve fiber indicator, superior average thickness, and inferior average thickness. There was no significant correlation between MD and GDx-VCC-measured parameters either in EG or GS eyes.

CONCLUSIONS

GDx VCC shows only moderate ability to distinguish normal eyes from eyes with early glaucoma. However, its diagnostic role in eyes with suspicious discs and normal visual fields is uncertain at this moment in the Taiwan Chinese population. Further studies are needed to address this issue.