Search

In recent years, computer vision, artificial intelligence, machine learning, and other high-tech technologies have advanced rapidly. These strategies lay a new technical foundation for online learning and intelligent education by making it easier to promote the scientific, intelligent, and data-driven growth of learners’ academic emotions. However, at present, online learning can better make up for the shortcomings of traditional learning and enable people to realize distance learning. However, as an important indicator, learners’ learning emotion has a direct impact on learners’ learning quality and effect. Therefore, this paper analyzes distance learners’ academic emotions based on online learning behavior data. It extracts online learning behavior data by using a deep learning algorithm and multimodal weighted feature fusion based on DS (Dempster-Shafer) evidence theory, establishing distance learners’ academic cognition motivation model, and constructs an online learning emotion measurement framework. Finally, it is determined through a correlation study of distance learners’ academic emotions and learning impacts those learners’ academic emotions in class. It will have a beneficial influence on learning since learners’ academic emotion is favorably connected with instructors’ emotion, and learners’ addition, deletion, and modification behavior is positively correlated with learners’ academic emotion.

Purpose: To propose a novel approach for automated measurements of margin reflex distance (MRD) using digital image analysis and to evaluate the agreement between automated and manual measurements of MRD. Materials and Methods: This observational study included 132 eyes of 66 volunteers referred to an oculoplastic clinic for blepharoptosis. Preoperative facial photographs of participants were taken. MRD1 and MRD2 were measured manually by a senior surgeon and automatically by our image-based algorithm. Correlation analyses and Bland-Altman analyses were performed to evaluate the agreement between the two measurements. Results: A strong positive correlation was observed between automated and manual MRD1 measurements, with Spearman's r = 0.968 (95% confidence interval [CI] = 0.949-0.976; P < .001) and intraclass correlation coefficient (ICC) = 0.966 (95% CI = 0.953-0.976; P < .001). The bias between automated and manual MRD1 measurements was 0.02 mm (95% CI = -0.06-0.10 mm), with 95% limits of agreement (LoA) = -0.88 to 0.92 mm. Agreement also existed in MRD2 measurements, with Spearman's r = 0.803 (95% CI = 0.716-0.865; P < .001), ICC = 0.833 (95% CI = 0.772-0.879; P < .001), and bias = 0.34 mm (95% CI = 0.26-0.42 mm), 95% LoA = -0.54 to 1.22 mm. Conclusions: Automated eyelid measurements in blepharoptosis using the image-based approach compare favorably with clinical manual measurements. This novel approach allows an objective assessment of ptosis with high accuracy.

Molecular flexure, and molecule-metal contact of para-sexiphenyl molecules on a Ag(111) surface are investigated by using low temperature scanning tunneling microscopy, and molecular manipulations. Atom trapping with sexiphenyl molecules is realized by laterally manipulating the molecules onto individual silver atoms and up to three silver atoms have been trapped. We also demonstrate breaking of a silver dimer into individual silver atoms by atom trapping. STM manipulation experiments show that the molecule-metal complexes formed by the atom trapping are mechanically stable. Moreover, Lateral manipulation of a single sexiphenyl across a Ag(111) atomic step highlights how the molecule moves across step-edges; the molecule can easily conform across the step and it recovers original configuration after the manipulation.

Objective: Many common eye diseases and cardiovascular diseases can be diagnosed through retinal imaging. However, due to uneven illumination, image blurring, and low contrast, retinal images with poor quality are not useful for diagnosis, especially in automated image analyzing systems. Here, we propose a new image enhancement method to improve color retinal image luminosity and contrast. Methods: A luminance gain matrix, which is obtained by gamma correction of the value channel in the HSV (hue, saturation, and value) color space, is used to enhance the R, G, and B (red, green and blue) channels, respectively. Contrast is then enhanced in the luminosity channel of L*a*b* color space by CLAHE (contrast-limited adaptive histogram equalization). Image enhancement by the proposed method is compared to other methods by evaluating quality scores of the enhanced images. Results: The performance of the method is mainly validated on a dataset of 961 poor-quality retinal images. Quality assessment (range 0–1) of image enhancement of this poor dataset indicated that our method improved color retinal image quality from an average of 0.0404 (standard deviation 0.0291) up to an average of 0.4565 (standard deviation 0.1000). Conclusion: The proposed method is shown to achieve superior image enhancement compared to contrast enhancement in other color spaces or by other related methods, while simultaneously preserving image naturalness. Significance: This method of color retinal image enhancement may be employed to assist ophthalmologists in more efficient screening of retinal diseases and in development of improved automated image analysis for clinical diagnosis.

Purpose Retinal imaging is an important and effective tool for detecting retinal diseases. However, degraded images caused by the aberrations of the eye can disguise lesions, so that a diseased eye can be mistakenly diagnosed as normal. In this work, we propose a new image enhancement method to improve the quality of degraded images. Methods A new method is used to enhance degraded-quality fundus images. In this method, the image is converted from the input RGB colour space to LAB colour space and then each normalized component is enhanced using contrast-limited adaptive histogram equalization. Human visual system (HVS)-based fundus image quality assessment, combined with diagnosis by experts, is used to evaluate the enhancement. Results The study included 191 degraded-quality fundus photographs of 143 subjects with optic media opacity. Objective quality assessment of image enhancement (range: 0–1) indicated that our method improved colour retinal image quality from an average of 0.0773 (variance 0.0801) to an average of 0.3973 (variance 0.0756). Following enhancement, area under curves (AUC) were 0.996 for the glaucoma classifier, 0.989 for the diabetic retinopathy (DR) classifier, 0.975 for the age-related macular degeneration (AMD) classifier and 0.979 for the other retinal diseases classifier. Conclusion The relatively simple method for enhancing degraded-quality fundus images achieves superior image enhancement, as demonstrated in a qualitative HVS-based image quality assessment. This retinal image enhancement may, therefore, be employed to assist ophthalmologists in more efficient screening of retinal diseases and the development of computer-aided diagnosis.

Background Asymmetric medullary veins (AMV) are frequently observed in stroke patients and single-echo susceptibility weighted imaging (SWIs) is the main technique in detecting AMV. Our study aimed to investigate which echo time (TE) on single-echo susceptibility is the optimal echo for visualizing AMV and to compare the ability in detecting AMV in stroke patients between SWIs and multi-echo susceptibility weighted imaging (SWIc). Materials and methods Twenty patients with middle cerebral artery stroke were included. SWI was acquired by using a multi-echo gradient-echo sequence with six echoes ranging from 5 ms to 35.240 ms. Three different echoes of SWIs including SWIs1 (TE = 23.144 ms), SWIs2 (TE = 29.192 ms) and SWIs3 (TE = 35.240 ms) were reconstructed. SWIc was averaged using the three echoes of SWIs. Image quality and venous contrast of medullary veins were compared between SWIs and SWIc using peak signal-to-noise ratio (PSNR), mean opinion score (MOS), contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). The presence of AMV was evaluated in each SWIs (1-3) and SWIc. Results SWIs2 had the highest PSNR, MOS and CNR and SWIs1 had the highest SNR among three different echoes of SWIs. No significant difference was found in SNR between SWIs1 and SWIs2. PSNR, MOS and CNR in SWIc were significantly increased by 27.9%, 28.2% and 17.2% compared with SWIs2 and SNR in SWIc was significantly increased by 32.4% compared with SWIs1. 55% of patients with AMV were detected in SWIs2, SWIs3 and SWIc, while 50% AMV were found in SWIs1. Conclusions SWIs using TE around 29ms was optimal in visualizing AMV. SWIc could improve image quality and venous contrast, but was equal to SWIs using a relative long TE in evaluating AMV. These results provide the technique basis for further research of AMV in stroke.

Telemedicine and the medical "big data" era in ophthalmology highlight the use of non-mydriatic ocular fundus photography, which has given rise to indispensable applications of portable fundus cameras. However, in the case of portable fundus photography, non-mydriatic image quality is more vulnerable to distortions, such as uneven illumination, color distortion, blur, and low contrast. Such distortions are called generic quality distortions. This paper proposes an algorithm capable of selecting images of fair generic quality that would be especially useful to assist inexperienced individuals in collecting meaningful and interpretable data with consistency. The algorithm is based on three characteristics of the human visual system--multi-channel sensation, just noticeable blur, and the contrast sensitivity function to detect illumination and color distortion, blur, and low contrast distortion, respectively. A total of 536 retinal images, 280 from proprietary databases and 256 from public databases, were graded independently by one senior and two junior ophthalmologists, such that three partial measures of quality and generic overall quality were classified into two categories. Binary classification was implemented by the support vector machine and the decision tree, and receiver operating characteristic (ROC) curves were obtained and plotted to analyze the performance of the proposed algorithm. The experimental results revealed that the generic overall quality classification achieved a sensitivity of 87.45% at a specificity of 91.66%, with an area under the ROC curve of 0.9452, indicating the value of applying the algorithm, which is based on the human vision system, to assess the image quality of non-mydriatic photography, especially for low-cost ophthalmological telemedicine applications.

A novel full-reference image quality assessment metric employing statistical local correlation as an indicator for quality quantification is presented. The local correlation is extracted in a wavelet domain and is pooled into an objective quality score. Simulation and testing results demonstrate that the proposed method not only outperforms the other methods in terms of high accuracy of image quality prediction, but also is consistent with the subjective evaluations.

A generalized relative quality (RQ) assessment scheme is proposed here based on the Bayesian inference theory, which is reasonable to make use of full reference (FR) algorithms when the evaluation of the quality of homogeneous medical images is required. Each FR algorithm is taken as a kernel to represent the level of quality. Although, various kernels generate different order of magnitude, a normalization process can rationalize the quality index within 0 and 1, where 1 represent the highest quality and 0 represents the lowest quality. To validate the performance of the proposed scheme, a series of reconstructed susceptibility weighted imaging images are collected, where each image has its subjective scale. Both experimental results and a ROC analysis show that the RQ obtained from the proposed scheme is consistent with subjective evaluation.

Laser ultrasonic technology has been paid more and more attention in the NDT field due to its outstanding ability of non-contact measurements. As a good vibration measurement technology, CFPI (confocal Fabry-Perot interferometer) method is sensitive to the laser frequency shift caused by the ultrasonic wave propagation. In this paper, a laser ultrasonic receiver system based on the CFPI method is established, and the system parameters are analyzed in order to obtain an optimal resolution. Also some initial experiment results are presented to certify this system ability.

A no-reference objective metric for image quality assessment by integrating the topographic independent components analysis into feature extraction is presented. By taking the topographic relationship among the initially independent features into consideration, it extracts the features of more sparsity or independency which is essentially related to inherent quality. Evaluation results demonstrate that the proposed metric is able to predict the image quality accurately across various distortion types.