Most of the classical mathematical methods for edge detection based on the derivative of the pixels of the original image are Gradient operators, Laplacian and Laplacian of Gaussian operators. Gradient based edge detection methods, such as Roberts, Sobel and Prewitts, have used two 2-D linear filters to process vertical edges and horizontal edges separately to approximate first-order derivative of pixel values of the image. The Laplacian edge detection method has used a 2-D linear filter to approximate second-order derivative of pixel values of the image. Major drawback of second-order derivative approach is that the response at and around the isolated pixel is much stronger. In this research study, a novel approach utilizing Shannon entropy other than the evaluation of derivates of the image in detecting edges in gray level images has been proposed. The proposed approach solves this problem at some extent. In the proposed method, we have used a suitable threshold value to segment the image and achieve the binary image. After this the proposed edge detector is introduced to detect and locate the edges in the image. A standard test image is used to compare the results of the proposed edge detector with the Laplacian of Gaussian edge detector operator. In order to validate the results, seven different kinds of test images are considered to examine the versatility of the proposed edge detector. It has been observed that the proposed edge detector works effectively for different gray scale digital images. The results of this study were quite promising

Background: Artificial intelligence (AI) for echocardiography requires training and validation to standards expected of humans. We developed an online platform and established the Unity Collaborative to build a dataset of expertise from 17 hospitals for training, validation, and standardization of such techniques. Methods: The training dataset consisted of 2056 individual frames drawn at random from 1265 parasternal long-axis video-loops of patients undergoing clinical echocardiography in 2015 to 2016. Nine experts labeled these images using our online platform. From this, we trained a convolutional neural network to identify keypoints. Subsequently, 13 experts labeled a validation dataset of the end-systolic and end-diastolic frame from 100 new video-loops, twice each. The 26-opinion consensus was used as the reference standard. The primary outcome was precision SD, the SD of the differences between AI measurement and expert consensus. Results: In the validation dataset, the AI’s precision SD for left ventricular internal dimension was 3.5 mm. For context, precision SD of individual expert measurements against the expert consensus was 4.4 mm. Intraclass correlation coefficient between AI and expert consensus was 0.926 (95% CI, 0.904–0.944), compared with 0.817 (0.778–0.954) between individual experts and expert consensus. For interventricular septum thickness, precision SD was 1.8 mm for AI (intraclass correlation coefficient, 0.809; 0.729–0.967), versus 2.0 mm for individuals (intraclass correlation coefficient, 0.641; 0.568–0.716). For posterior wall thickness, precision SD was 1.4 mm for AI (intraclass correlation coefficient, 0.535 [95% CI, 0.379–0.661]), versus 2.2 mm for individuals (0.366 [0.288–0.462]). We present all images and annotations. This highlights challenging cases, including poor image quality and tapered ventricles. Conclusions: Experts at multiple institutions successfully cooperated to build a collaborative AI. This performed as well as individual experts. Future echocardiographic AI research should use a consensus of experts as a reference. Our collaborative welcomes new partners who share our commitment to publish all methods, code, annotations, and results openly.

In this article, a planar antenna based on Fibonacci word fractal geometry with defected ground structure (DGS) has been investigated. A novel type of antenna radiator is proposed using Fibonacci word fractal which works for public safety and dedicated short range public safety applications. Size of proposed antenna is 50 × 44 mm and it resonates at 4.9 and 5.9 GHz having bandwidth ranging from 4.8 to 5.1 GHz and 5.8 to 6.8 GHz, respectively. Defected Ground Structure is used to improve gain for lower frequency band. Representative results of fractal patch antenna are reported to access the effectiveness of the developed approach in public safety applications. Simulation and experimental result confirms the efficiency of Whale Optimization Algorithm for antenna design. Experimentally measured results are matches well with simulated results and quite promising.

Methylene blue USP (MB) is a FDA-grandfathered drug used in clinics to treat methemoglobinemia, carbon monoxide poisoning and cyanide poisoning that has been shown to increase fMRI evoked blood oxygenation level dependent (BOLD) response in rodents. Low dose MB also has memory enhancing effect in rodents and humans. However, the neural correlates of the effects of MB in the human brain are unknown. We tested the hypothesis that a single low oral dose of MB modulates the functional connectivity of neural networks in healthy adults. Task-based and task-free fMRI were performed before and one hour after MB or placebo administration utilizing a randomized, double-blinded, placebo-controlled design. MB administration was associated with a reduction in cerebral blood flow in a task-related network during a visuomotor task, and with stronger resting-state functional connectivity in multiple regions linking perception and memory functions. These findings demonstrate for the first time that low-dose MB can modulate task-related and resting-state neural networks in the human brain. These neuroimaging findings support further investigations in healthy and disease populations.

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