Comparative Study of Skin Color Detection and Segmentation in HSV and YCbCr Color Space

AbstractThis paper presented a comparative study of human skin color detection HSV and YCbCr color space. Skin color detection is the process of separation between skin and non-skin pixels. It is difficult to develop uniform method for the segmentation or detection of human skin detection because the color tone of human skin is drastically varied for people from one region to another. Literature survey shows that there is a variety of color space is applied for the skin color detection. RGB color space is not preferred for color based detection and color analysis because of mixing of color (chrominance) and intensity (luminance) information and its non uniform characteristics. Luminance and Hue based approaches discriminate color and intensity information even under uneven illumination conditions. Experimental result shows the efficiency of YCbCr color space for the segmentation and detection of skin color in color images

Controlled Radical Polymerization of Vinyl Acetate Mediated by a Bis(imino)pyridine Vanadium Complex

Source type: Prin

Pharmacokinetics of topically applied sparfloxacin in rabbits

<b>PURPOSE:</b> Fluoroquinolones are antimicrobial agents that have a broad spectrum of activity and are widely used against many of the ocular pathogens, responsible for conjunctivitis, blepharitis, corneal ulcers etc. The aim of our study was to evaluate the ocular pharmacokinetics of sparfloxacin (0.3&#x0025; w/v) in the aqueous humour of rabbits. <b> MATERIALS AND METHODS:</b> Pharmacokinetics of topically administered sparfloxacin were determined after a single application of 50 &#x00B5;l topically. The aqueous humour samples were collected at 0, 0.25, 0.5, 1, 2, 3, 4, 5 or 6 hours after instillation. High Performance Thin Layer Chromatographic method was used to analyse the drug concentration in the aqueous humour samples. <b> RESULTS:</b> Fifteen minutes after the instillation of 50 &#x00B5;l of sparfloxacin 0.3&#x0025; solution, the mean concentration in aqueous humour was found to be 1.4 &#x00B5;g/ml, which reaches the peak level of 3.7 &#x00B5;g/ml after 1.3 hours. At 6 hours, the sparfloxacin aqueous levels were 0.562 &#x00B5;g/ml. The clinical efficacy was predicted based on the Maximum Concentration (Cmax): Minimum Inhibitory Concentration (MIC) and Area Under the Concentration-time curve (AUC):MIC ratios. <b> CONCLUSION:</b> The sparfloxacin levels in aqueous humour of rabbits are sufficiently high up to the 6 hours after instillation in the conjunctival sac to provide bactericidal effect against most of the ocular pathogens. Both Cmax:MIC and AUC:MIC ratios are high enough to provide bactericidal effect against most of the ocular pathogens. Sparfloxacin (0.3&#x0025;) ophthalmic preparation has excellent penetration through cornea

Universal architecture of corneal segmental tomography biomarkers for artificial intelligence-driven diagnosis of early keratoconus

Aims To develop a comprehensive three-dimensional analyses of segmental tomography (placido and optical coherence tomography) using artificial intelligence (AI). Methods Preoperative imaging data (MS-39, CSO, Italy) of refractive surgery patients with stable outcomes and diagnosed with asymmetric or bilateral keratoconus (KC) were used. The curvature, wavefront aberrations and thickness distributions were analysed with Zernike polynomials (ZP) and a random forest (RF) AI model. For training and cross-validation, there were groups of healthy (n=527), very asymmetric ectasia (VAE; n=144) and KC (n=454). The VAE eyes were the fellow eyes of KC patients but no further manual segregation of these eyes into subclinical or forme-fruste was performed. Results The AI achieved an excellent area under the curve (0.994), accuracy (95.6%), recall (98.5%) and precision (92.7%) for the healthy eyes. For the KC eyes, the same were 0.997, 99.1%, 98.7% and 99.1%, respectively. For the VAE eyes, the same were 0.976, 95.5%, 71.5% and 91.2%, respectively. Interestingly, the AI reclassified 36 (subclinical) of the VAE eyes as healthy though these eyes were distinct from healthy eyes. Most of the remaining VAE (n=104; forme fruste) eyes retained their classification, and were distinct from both KC and healthy eyes. Further, the posterior surface features were not among the highest ranked variables by the AI model. Conclusions A universal architecture of combining segmental tomography with ZP and AI was developed. It achieved an excellent classification of healthy and KC eyes. The AI efficiently classified the VAE eyes as 'subclinical' and 'forme-fruste'

Universal architecture of corneal segmental tomography biomarkers for artificial intelligence-driven diagnosis of early keratoconus

Aims To develop a comprehensive three-dimensional analyses of segmental tomography (placido and optical coherence tomography) using artificial intelligence (AI). Methods Preoperative imaging data (MS-39, CSO, Italy) of refractive surgery patients with stable outcomes and diagnosed with asymmetric or bilateral keratoconus (KC) were used. The curvature, wavefront aberrations and thickness distributions were analysed with Zernike polynomials (ZP) and a random forest (RF) AI model. For training and cross-validation, there were groups of healthy (n=527), very asymmetric ectasia (VAE; n=144) and KC (n=454). The VAE eyes were the fellow eyes of KC patients but no further manual segregation of these eyes into subclinical or forme-fruste was performed. Results The AI achieved an excellent area under the curve (0.994), accuracy (95.6%), recall (98.5%) and precision (92.7%) for the healthy eyes. For the KC eyes, the same were 0.997, 99.1%, 98.7% and 99.1%, respectively. For the VAE eyes, the same were 0.976, 95.5%, 71.5% and 91.2%, respectively. Interestingly, the AI reclassified 36 (subclinical) of the VAE eyes as healthy though these eyes were distinct from healthy eyes. Most of the remaining VAE (n=104; forme fruste) eyes retained their classification, and were distinct from both KC and healthy eyes. Further, the posterior surface features were not among the highest ranked variables by the AI model. Conclusions A universal architecture of combining segmental tomography with ZP and AI was developed. It achieved an excellent classification of healthy and KC eyes. The AI efficiently classified the VAE eyes as 'subclinical' and 'forme-fruste'

Repeatability and Agreement of a New Scheimpflug Device and a Hartmann-Shack Aberrometer With a Ray-Tracing Aberrometer in Normal, Keratoconus, and CXL Groups

PURPOSE: To assess repeatability and agreement of a Scheimpflug topographer and a Hartmann-Shack aberrometer with a ray-tracing aberrometer in normal, keratoconus, and corneal cross-linking (CXL) groups. METHODS: In this cross-sectional, observational study, normal, keratoconus, and CXL groups with 125 eyes in each of the three groups were included. All eyes underwent three sets of measurements, at a single visit, using the Pentacam AXL Wave (Oculus Optikgerate GmbH) and iTrace (Tracey Technologies). Keratometry, pachymetry, objective refraction, and total ocular aberrations including root mean square (RMS) lower order aberrations (LOAs), RMS higher order aberrations (HOAs), RMS coma, and spherical aberrations (SA) were analyzed. Objective assessment of haze after CXL was done using the Pentacam AXL Wave. Repeatability was assessed using within-subject standard deviation (Sw), test-retest variability, within-subject coefficient of variation (COV), and intraclass correlation coefficient (ICC). Bland-Altman analysis assessed 95% limits of agreement and correlation coefficient (r) determined the strength of the relationship between measurements. RESULTS: The Pentacam AXL Wave had Sw for keratometry of 0.12 in the normal group and 0.15 in the keratoconus group and lower (poorer) Sw of 0.17 in the CXL group. For pachymetry, Sw was 9.18, 9.53, and 10.11 in the normal, keratoconus, and CXL groups, respectively. Total aberrations had ICCs ranging from 0.88 for RMS HOAs to 0.95 for SA in the normal group, 0.86 for RMS HOAs to 0.92 for SA in the keratoconus group, and 0.72 for RMS HOAs to 0.82 for SA (poorer) in the CXL group. In the normal group, mean differences between the two devices were nonsignificant for all parameters except SA (0.011 +/- 0.038 mu m, P < .01; limits of agreement =-0.09 to 0.07; r = 0.87). In the keratoconus group, mean differences were significant in all aberrations except RMS LOAs (-0.27 +/- 0.85 mu m, P = .10; limits of agreement =-3.3 to 3.8; r = 0.92). In the CXL group, all parameters varied significantly (P < .006). CONCLUSIONS: The Pentacam AXL Wave showed comparable repeatability in the normal and keratoconus groups, but was poorer in the CXL group, more so with increasing corneal haze. Both devices can be used interchangeably in normal eyes, except for SA, but not in eyes with keratoconus or CXL for aberration measurements. [J Refract Surg. 2022;38(3):201-208.