Précis: The C3 fields analyzer (CFA) is a moderately reliable perimeter preferred by patients to standard perimetry. While it does not approximate the gold standard, it was sensitive and specific for clinically defined glaucoma (area under the receiving operator characteristic curve=0.77 to 0.86). Purpose: Testing the visual field is a vital sign for diagnosing and managing glaucoma. The current gold standard, the Humphrey visual field analyzer (HFA), is large, expensive and can be uncomfortable for some patients. The current study investigated the CFA, a virtual reality head-mounted visual field testing device, as a possible subjective field test for glaucoma screening and eventually glaucoma monitoring. Patients and Methods: The CFA presented stimuli in the same 54 positions as the HFA 24-2 SITA Standard test using a suprathreshold algorithm approximating an 18 dB deficit. A total of 157 patients (both controls and glaucoma patients) at the Aravind Eye Hospital, Pondicherry, India, were tested with both devices. Results: The number of stimuli missed on the CFA correlated with HFA mean deviation (r=0.62, P<0.001), and with pattern standard deviation (r=0.36, P<0.001). The area under the receiving operator characteristic curve was 0.77±0.06 for mild glaucoma (HFA mean deviation ≥−6 dB) and 0.86±0.04 for moderate-advanced glaucoma (HFA mean deviation <−6 dB). Patients with an 18 dB or worse deficit at a point in the visual field on the HFA failed to see the CFA stimulus at the same position 38% of the time. Conclusions: While the CFA did not reliably identify deficits that matched the HFA, it was moderately effective at identifying glaucoma subjects. Further refinements to the device will be required to improve point by point testing performance and screening performance.

Respiratory rate (RR) has been shown to be a reliable predictor of cardio-pulmonary deterioration, but standard RR monitoring methods in the neonatal intensive care units (NICU) with contact leads have been related to iatrogenic complications. Video-based monitoring is a potential non-contact system that could improve patient care. This iterative design study developed a novel algorithm that produced RR from footage analyzed from stable NICU patients in open cribs with corrected gestational ages ranging from 33 to 40 weeks. The final algorithm used a proprietary technique of micromotion and stationarity detection (MSD) to model background noise to be able to amplify and record respiratory motions. We found significant correlation—r equals 0.948 (p value of 0.001)—between MSD and the current hospital standard, electrocardiogram impedance pneumography. Our video-based system showed a bias of negative 1.3 breaths and root mean square error of 6.36 breaths per minute compared to standard continuous monitoring. Further work is needed to evaluate the ability of video-based monitors to observe clinical changes in a larger population of patients over extended periods of time.

Underexposed heterogeneous complex-background and graphical embossing text documents are treated using proposed preprocessing image-abstraction framework that can deliver the effective structure preserved abstracted output by manipulating visual-features from input images. Reading of the text character in such images is extremely poor; hence, the framework effectively boosted the significant image properties and quality features at every stage. Work effectively preserves the foreground structure of an image by comprehensively integrating the sequence of NPR filters and diminishes the background content of an image, and in this way, the framework contributes to separation of foreground text from image background. Effectiveness of the proposed work has been validated by conducting the trials on the selected dataset. In addition, user's visual-feedback and image quality assessment techniques were also used to evaluate the framework. Based on the obtained abstraction output, this work extracts text-character by wisely utilizing traditional image processing techniques with an average accuracy of 98.91%.

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