PurposeTest–retest variability (TRV) of visual field (VF) data seriously degrades our capacity to recognize true VF progression. We conducted repeated high-resolution perimetry with a test interval of 0.5° to investigate the sources of TRV. In particular, we examined whether the spatial variance of the observed sensitivity changes or if their absolute magnitude was of more importance.MethodsSixteen eyes of 16 glaucoma patients were each tested three times at 61 VF locations along the superior-temporal 45° meridian using a modified protocol of the Octopus 900 perimeter. TRV was quantified as the standard deviation of the repeats at each point (retest-SD). We also computed the mean sensitivity at each point (retest-MS) and the running spatial-SD along the tested meridian. Multiple regression models investigated whether any of those variables (and also age, sex, and VF eccentricity) were significant independent determinants of TRV.ResultsThe main independent determinants of TRV were the retest-MS at −0.04 dB TRV/dB loss (P < 0.0001, t-statistic 5.05), and the retest-SD at 0.47 dB spatial variance/dB loss (P < 0.0001, t-statistic 12.5).ConclusionsThe larger effect for the spatial-SD suggested that it was perhaps a stronger determinant of TRV than scotoma depth per se. This might support the hypothesis that interactions between small perimetric stimuli, rapidly varying sensitivity across the field, and normal fixational jitter are strong determinants of TRV.Translational RelevanceOur study indicates that methods that might reduce the effects of jagged sensitivity changes, such as increasing stimulus size or better gaze tracking, could reduce TRV.

In this paper, the authors present a markerless vision-based estimation and control algorithm for a micro helicopter with a wireless camera. The camera looks downward, and it captures an image of the ground at each time step. The proposed algorithm consists of vision-based estimation and switched control. The 3D location and posture of the helicopter are estimated by using template matching with automatic reference generation. The proposed algorithm also detects noisy images that are sometimes captured by the camera. The control signals are produced by a set of PID controllers if the current captured image is noiseless, and otherwise by a set of integral controllers. Long-time hovering is achieved over a high contrast and planar ground. No specified markers or prior texture information are required. Yasushi IWATANI (Member)He received his B.S.

PurposeA newly developed head-mounted perimeter termed “imo” enables visual field (VF) testing without a fixed head position. Because the positional relationship between the subject’s head and the imo is fixed, the effects of head position changes on the test results are small compared with those obtained using a stationary perimeter. However, only ocular counter-roll (OCR) induced by head tilt might affect VF testing. To quantitatively reveal the effects of head tilt and OCR on the VF test results, we investigated the associations among the head-tilt angle, OCR amplitude and VF testing results.Subjects and methodsFor 20 healthy subjects, we binocularly recorded static OCR (s-OCR) while tilting the subject’s head at an arbitrary angle ranging from 0° to 60° rightward or leftward in 10° increments. By monitoring iris patterns, we evaluated the s-OCR amplitude. We also performed blind spot detection while tilting the subject’s head by an arbitrary angle ranging from 0° to 50° rightward or leftward in 10° increments to calculate the angle by which the blind spot rotates because of head tilt.ResultsThe association between s-OCR amplitude and head-tilt angle showed a sinusoidal relationship. In blind spot detection, the blind spot rotated to the opposite direction of the head tilt, and the association between the rotation angle of the blind spot and the head-tilt angle also showed a sinusoidal relationship. The rotation angle of the blind spot was strongly correlated with the s-OCR amplitude (R2≥0.94, p<0.0001). A head tilt greater than 20° with imo causes interference between adjacent test areas.ConclusionsBoth the s-OCR amplitude and the rotation angle of the blind spot were correlated with the head-tilt angle by sinusoidal regression. The rotated VF was correlated with the s-OCR amplitude. During perimetry using imo, the change in the subject’s head tilt should be limited to 20°.

To detect subtle VF defects within the eccentricity of 10 degrees, high-resolution perimetry with the test point resolution of <1.5 degrees is necessary.