Exploring the optimum step size for defocus curves

Purpose: To evaluate the effect of reducing the number of visual acuity measurements made in a defocus curve on the quality of data quantified. Setting: Midland Eye, Solihull, United Kingdom. Design: Evaluation of a technique. Methods: Defocus curves were constructed by measuring visual acuity on a distance logMAR letter chart, randomizing the test letters between lens presentations. The lens powers evaluated ranged between +1.50 diopters (D) and -5.00 D in 0.50 D steps, which were also presented in a randomized order. Defocus curves were measured binocularly with the Tecnis diffractive, Rezoom refractive, Lentis rotationally asymmetric segmented (+3.00 D addition [add]), and Finevision trifocal multifocal intraocular lenses (IOLs) implanted bilaterally, and also for the diffractive IOL and refractive or rotationally asymmetric segmented (+3.00 D and +1.50 D adds) multifocal IOLs implanted contralaterally. Relative and absolute range of clear-focus metrics and area metrics were calculated for curves fitted using 0.50 D, 1.00 D, and 1.50 D steps and a near add-specific profile (ie, distance, half the near add, and the full near-add powers). Results: A significant difference in simulated results was found in at least 1 of the relative or absolute range of clear-focus or area metrics for each of the multifocal designs examined when the defocus-curve step size was increased (P<.05). Conclusion: Faster methods of capturing defocus curves from multifocal IOL designs appear to distort the metric results and are therefore not valid. Financial Disclosure: No author has a financial or proprietary interest in any material or method mentioned. © 2013 ASCRS and ESCRS.

Application of ensemble averaging to the analysis of electromyography recordings under whole body vibratory stimulation

The aim of this study is to evaluate the application of ensemble averaging to the analysis of electromyography recordings under whole body vibratory stimulation. Recordings from Rectus Femoris, collected during vibratory stimulation at different frequencies, are used. Each signal is subdivided in intervals, which time duration is related to the vibration frequency. Finally the average of the segmented intervals is performed. By using this method for the majority of the recordings the periodic components emerge. The autocorrelation of few seconds of signals confirms the presence of a pseudosinusoidal components strictly related to the soft tissues oscillations caused by the mechanical waves. © 2014 IEEE.

A laser induced breakdown spectroscopy quantitative analysis method based on the robust least squares support vector machine regression model

Data fluctuation in multiple measurements of Laser Induced Breakdown Spectroscopy (LIBS) greatly affects the accuracy of quantitative analysis. A new LIBS quantitative analysis method based on the Robust Least Squares Support Vector Machine (RLS-SVM) regression model is proposed. The usual way to enhance the analysis accuracy is to improve the quality and consistency of the emission signal, such as by averaging the spectral signals or spectrum standardization over a number of laser shots. The proposed method focuses more on how to enhance the robustness of the quantitative analysis regression model. The proposed RLS-SVM regression model originates from the Weighted Least Squares Support Vector Machine (WLS-SVM) but has an improved segmented weighting function and residual error calculation according to the statistical distribution of measured spectral data. Through the improved segmented weighting function, the information on the spectral data in the normal distribution will be retained in the regression model while the information on the outliers will be restrained or removed. Copper elemental concentration analysis experiments of 16 certified standard brass samples were carried out. The average value of relative standard deviation obtained from the RLS-SVM model was 3.06% and the root mean square error was 1.537%. The experimental results showed that the proposed method achieved better prediction accuracy and better modeling robustness compared with the quantitative analysis methods based on Partial Least Squares (PLS) regression, standard Support Vector Machine (SVM) and WLS-SVM. It was also demonstrated that the improved weighting function had better comprehensive performance in model robustness and convergence speed, compared with the four known weighting functions.

Handwritten and machine-printed text discrimination using a template matching approach

We propose a novel template matching approach for the discrimination of handwritten and machine-printed text. We first pre-process the scanned document images by performing denoising, circles/lines exclusion and word-block level segmentation. We then align and match characters in a flexible sized gallery with the segmented regions, using parallelised normalised cross-correlation. The experimental results over the Pattern Recognition & Image Analysis Research Lab-Natural History Museum (PRImA-NHM) dataset show remarkably high robustness of the algorithm in classifying cluttered, occluded and noisy samples, in addition to those with significant high missing data. The algorithm, which gives 84.0% classification rate with false positive rate 0.16 over the dataset, does not require training samples and generates compelling results as opposed to the training-based approaches, which have used the same benchmark.

Can aberrometry provide rapid and reliable measures of subjective depth of focus following multifocal intraocular lens implantation?

Purpose: To determine whether the ‘through-focus’ aberrations of a multifocal and accommodative intraocular lens (IOL) implanted patient can be used to provide rapid and reliable measures of their subjective range of clear vision. Methods: Eyes that had been implanted with a concentric (n = 8), segmented (n = 10) or accommodating (n = 6) intraocular lenses (mean age 62.9 ± 8.9 years; range 46-79 years) for over a year underwent simultaneous monocular subjective (electronic logMAR test chart at 4m with letters randomised between presentations) and objective (Aston open-field aberrometer) defocus curve testing for levels of defocus between +1.50 to -5.00DS in -0.50DS steps, in a randomised order. Pupil size and ocular aberration (a combination of the patient’s and the defocus inducing lens aberrations) at each level of blur was measured by the aberrometer. Visual acuity was measured subjectively at each level of defocus to determine the traditional defocus curve. Objective acuity was predicted using image quality metrics. Results: The range of clear focus differed between the three IOL types (F=15.506, P=0.001) as well as between subjective and objective defocus curves (F=6.685, p=0.049). There was no statistically significant difference between subjective and objective defocus curves in the segmented or concentric ring MIOL group (P>0.05). However a difference was found between the two measures and the accommodating IOL group (P<0.001). Mean Delta logMAR (predicted minus measured logMAR) across all target vergences was -0.06 ± 0.19 logMAR. Predicted logMAR defocus curves for the multifocal IOLs did not show a near vision addition peak, unlike the subjective measurement of visual acuity. However, there was a strong positive correlation between measured and predicted logMAR for all three IOLs (Pearson’s correlation: P<0.001). Conclusions: Current subjective procedures are lengthy and do not enable important additional measures such as defocus curves under differently luminance or contrast levels to be assessed, which may limit our understanding of MIOL performance in real-world conditions. In general objective aberrometry measures correlated well with the subjective assessment indicating the relative robustness of this technique in evaluating post-operative success with segmented and concentric ring MIOL.