Ultrasonographic features of PMEL17 ( Silver ) mutant gene-associated multiple congenital ocular anomalies (MCOA) in Comtois and Rocky Mountain horses

OBJECTIVE: (1) To describe the ultrasonographic appearance of multiple congenital ocular anomalies (MCOA) in the eyes of horses with the PMEL17 (Silver) mutant gene. (2) To compare the accuracy of B-mode ocular ultrasound to conventional direct ophthalmoscopy. ANIMALS STUDIED: Sixty-seven Comtois and 18 Rocky Mountain horses were included in the study. PROCEDURES: Horses were classified as being carriers or noncarriers of the PMEL17 mutant allele based on coat color or genetic testing. Direct ophthalmoscopy followed by standardized ultrasonographic examination was performed in all horses. RESULTS: Seventy-five of 85 horses (88.24%) carried at least one copy of the Silver mutant allele. Cornea globosa, severe iridal hypoplasia, uveal cysts, cataracts, and retinal detachment could be appreciated with ultrasound. Carrier horses had statistically significantly increased anterior chamber depth and decreased thickness of anterior uvea compared with noncarriers (P < 0.05). Uveal cysts had a wide range of location and ultrasonographic appearances. In 51/73 (69.86%) carrier horses, ultrasound detected ciliary cysts that were missed with direct ophthalmoscopy. CONCLUSIONS: In this study, ultrasonography was useful to identify uveal cysts in PMEL17 mutant carriers and to assess anterior chamber depth.

Robust Denoising using Feature and Color Information

We propose a method that robustly combines color and feature buffers to denoise Monte Carlo renderings. On one hand, feature buffers, such as per pixel normals, textures, or depth, are effective in determining denoising filters because features are highly correlated with rendered images. Filters based solely on features, however, are prone to blurring image details that are not well represented by the features. On the other hand, color buffers represent all details, but they may be less effective to determine filters because they are contaminated by the noise that is supposed to be removed. We propose to obtain filters using a combination of color and feature buffers in an NL-means and cross-bilateral filtering framework. We determine a robust weighting of colors and features using a SURE-based error estimate. We show significant improvements in subjective and quantitative errors compared to the previous state-of-the-art. We also demonstrate adaptive sampling and space-time filtering for animations.

A Food Recognition System for Diabetic Patients based on an Optimized Bag of Features Model

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the Bag of Features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5,000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10,000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.

SPECTRAL DOMAIN-OPTICAL COHERENCE TOMOGRAPHY IMAGE CONTRAST AND BACKGROUND COLOR SETTINGS INFLUENCE IDENTIFICATION OF RETINAL STRUCTURES.

PURPOSE To evaluate image contrast and color setting on assessment of retinal structures and morphology in spectral-domain optical coherence tomography. METHODS Two hundred and forty-eight Spectralis spectral-domain optical coherence tomography B-scans of 62 patients were analyzed by 4 readers. B-scans were extracted in 4 settings: W + N = white background with black image at normal contrast 9; W + H = white background with black image at maximum contrast 16; B + N = black background with white image at normal contrast 12; B + H = black background with white image at maximum contrast 16. Readers analyzed the images to identify morphologic features. Interreader correlation was calculated. Differences between Fleiss-kappa correlation coefficients were examined using bootstrap method. Any setting with significantly higher correlation coefficient was deemed superior for evaluating specific features. RESULTS Correlation coefficients differed among settings. No single setting was superior for all respective spectral-domain optical coherence tomography parameters (P = 0.3773). Some variables showed no differences among settings. Hard exudates and subretinal fluid were best seen with B + H (κ = 0.46, P = 0.0237 and κ = 0.78, P = 0.002). Microaneurysms were best seen with W + N (κ = 0.56, P = 0.025). Vitreomacular interface, enhanced transmission signal, and epiretinal membrane were best identified using all color/contrast settings together (κ = 0.44, P = 0.042, κ = 0.57, P = 0.01, and κ = 0.62, P ≤ 0.0001). CONCLUSION Contrast and background affect the evaluation of retinal structures on spectral-domain optical coherence tomography images. No single setting was superior for all features, though certain changes were best seen with specific settings.