Advancing Bag-of-visual-words Representations For Lesion Classification In Retinal Images.

Diabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not readily discovered. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the identification of lesions must be accurate to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm employs a maximum-margin classifier in a flexible framework that is able to detect the most common DR-related lesions such as microaneurysms, cotton-wool spots and hard exudates. BoVW allows to bypass the need for pre- and post-processing of the retinographic images, as well as the need of specific ad hoc techniques for identification of each type of lesion. An extensive evaluation of the BoVW model, using three large retinograph datasets (DR1, DR2 and Messidor) with different resolution and collected by different healthcare personnel, was performed. The results demonstrate that the BoVW classification approach can identify different lesions within an image without having to utilize different algorithms for each lesion reducing processing time and providing a more flexible diagnostic system. Our BoVW scheme is based on sparse low-level feature detection with a Speeded-Up Robust Features (SURF) local descriptor, and mid-level features based on semi-soft coding with max pooling. The best BoVW representation for retinal image classification was an area under the receiver operating characteristic curve (AUC-ROC) of 97.8% (exudates) and 93.5% (red lesions), applying a cross-dataset validation protocol. To assess the accuracy for detecting cases that require referral within one year, the sparse extraction technique associated with semi-soft coding and max pooling obtained an AUC of 94.2 ± 2.0%, outperforming current methods. Those results indicate that, for retinal image classification tasks in clinical practice, BoVW is equal and, in some instances, surpasses results obtained using dense detection (widely believed to be the best choice in many vision problems) for the low-level descriptors.

The Lea Grating Test In Assessing Detection Grating Acuity In Normal Infants Less Than 4 Months Of Age.

To assess binocular detection grating acuity using the LEA GRATINGS test to establish age-related norms in healthy infants during their first 3 months of life. In this prospective, longitudinal study of healthy infants with clear red reflex at birth, responses to gratings were measured at 1, 2, and 3 months of age using LEA gratings at a distance of 28 cm. The results were recorded as detection grating acuity values, which were arranged in frequency tables and converted to a one-octave scale for statistical analysis. For the repeated measurements, analysis of variance (ANOVA) was used to compare the detection grating acuity results between ages. A total of 133 infants were included. The binocular responses to gratings showed development toward higher mean values and spatial frequencies, ranging from 0.55 ± 0.70 cycles per degree (cpd), or 1.74 ± 0.21 logMAR, in month 1 to 3.11 ± 0.54 cpd, or 0.98 ± 0.16 logMAR, in month 3. Repeated ANOVA indicated differences among grating acuity values in the three age groups. The LEA GRATINGS test allowed assessment of detection grating acuity and its development in a cohort of healthy infants during their first 3 months of life.

Comparative Study Of Transgenic And Non-transgenic Maize (zea Mays) Flours Commercialized In Brazil, Focussing On Proteomic Analyses.

Genetically modified foods are a major concern around the world due to the lack of information concerning their safety and health effects. This work evaluates differences, at the proteomic level, between two types of crop samples: transgenic (MON810 event with the Cry1Ab gene, which confers resistance to insects) and non-transgenic maize flour commercialized in Brazil. The 2-D DIGE technique revealed 99 differentially expressed spots, which were collected in 2-D PAGE gels and identified via mass spectrometry (nESI-QTOF MS/MS). The abundance of protein differences between the transgenic and non-transgenic samples could arise from genetic modification or as a result of an environmental influence pertaining to the commercial sample. The major functional category of proteins identified was related to disease/defense and, although differences were observed between samples, no toxins or allergenic proteins were found.