Recognition of Images Degraded by Gaussian Blur

Binary classification model of machine learning detected altered gut integrity in controlled-cortical impact model of traumatic brain injury

Aim of the study: To examine the effect of controlled-cortical impact (CCI), a preclinical model of traumatic brain injury (TBI), on intestinal integrity using a binary classification model of machine learning (ML).Materials and methods: Adult, male C57BL/6 mice were subjected to CCI surgery using a stereotaxic impactor (Impact One™). The rotarod and hot-plate tests were performed to assess the neurological deficits.Results: Mice underwent CCI displayed a remarkable neurological deficit as noticed by decreased latency to fall and lesser paw withdrawal latency in rotarod and hot plate test, respectively. Animals were sacrificed 3 days post-injury (dpi). The colon sections were stained with hematoxylin and eosin (H&E) to integrate with machinery tool-based algorithms. Several stained colon images were captured to build a dataset for ML model to predict the impact of CCI vs sham procedure. The best results were obtained with VGG16 features with SVM RBF kernel and VGG16 features with stacked fully connected layers on top. We achieved a test accuracy of 84% and predicted the disrupted gut permeability and epithelium wall of colon in CCI group as compared to sham-operated mice.Conclusion: We suggest that ML may become an important tool in the development of preclinical TBI model and discovery of newer therapeutics.

C-COVIDNet: A CNN Model for COVID-19 Detection Using Image Processing

COVID-19 has become a global disaster that has disturbed the socioeconomic fabric of the world. Efficient and cost-effective diagnosis methods are very much required for better treatment and eliminating false cases for COVID-19. COVID-19 disease is a type of respiratory syndrome, thus lung X-ray analysis has got the attention for an effective diagnosis. Hence, the proposed study introduces an Image processing based COVID-19 detection model C-COVIDNet, which is trained on a dataset of chest X-ray images belonging to three categories: COVID-19, Pneumonia, and Normal person. Image preprocessing pipeline is used for extracting the region of interest (ROI), so that the required features may be present in the input. This lightweight convolution neural network (CNN) based approach has achieved an accuracy of 97.5% and an F1-score of 97.91%. Model input images are generated in batches using a custom data generator. The performance of C-COVIDNet has outperformed the state-of-the-art. The promising results will surely help in accelerating the development of deep learning-based COVID-19 diagnosis tools using radiography.

A Machine Learning Method for Automated In Vivo Transparent Vessel Segmentation and Identification Based on Blood Flow Characteristics

In vivo transparent vessel segmentation is important to life science research. However, this task remains very challenging because of the fuzzy edges and the barely noticeable tubular characteristics of vessels under a light microscope. In this paper, we present a new machine learning method based on blood flow characteristics to segment the global vascular structure in vivo. Specifically, the videos of blood flow in transparent vessels are used as input. We use the machine learning classifier to classify the vessel pixels through the motion features extracted from moving red blood cells and achieve vessel segmentation based on a region-growing algorithm. Moreover, we utilize the moving characteristics of blood flow to distinguish between the types of vessels, including arteries, veins, and capillaries. In the experiments, we evaluate the performance of our method on videos of zebrafish embryos. The experimental results indicate the high accuracy of vessel segmentation, with an average accuracy of 97.98%, which is much more superior than other segmentation or motion-detection algorithms. Our method has good robustness when applied to input videos with various time resolutions, with a minimum of 3.125 fps.

Image Analysis by Fractional-Order Gaussian-Hermite Moments

Moments and moment invariants are effective feature descriptors. They have widespread applications in the field of image processing. The recent researches show that fractional-order moments have notable image representation ability. Hermite polynomials are defined over the interval from negative infinity to positive one. Such unboundedness prevents us from developing fractional-order Gaussian-Hermite moments via the existing ideas or approaches. In this paper, we propose fractional-order Gaussian-Hermite moments by forcing the definition domain of Hermite polynomials to be a bounded interval, meanwhile, resorting to a value-decreasing standard deviation to maintain the orthogonality. Moreover, we successfully develop contrast, translation and rotation invariants from the proposed moments based on the inherent properties of Hermite polynomials. The reconstructions of different types of images demonstrate that the proposed moments have more superior image representation ability to the most existing popular orthogonal moments. Besides, the salient performance in invariant image recognition, noise robustness and region-of-interest feature extraction reflect that these moments and their invariants possess the stronger discrimination power and the better noise robustness in comparison with the existing orthogonal moments. Furthermore, both complexity analysis and time consumption indicate that the proposed moments and their invariants are easy to implement, they are suitable for practical engineering applications.

Edge-based effective active appearance model for real-time wrinkle detection

Background

Recently, the field of face and facial features has been progressively studied. The features of facial expression have gained increasing attention for related applications. The wrinkle is the most representative feature, and its research and applications have been topics of high interest. Wrinkles play an important role in face feature analysis. They have been widely used in applications, such as age estimation, skin texture classification, expression recognition, and simulation.

Purpose

Existing approaches to the image‐based analysis of wrinkles as texture not as curvilinear discontinuity and wrinkle detection mainly have focused on detecting wrinkles on forehead position, which is usually horizontal linear shapes, while the detection of the nasolabial wrinkle is not well understood due to their variety of shapes and complexity.

Method

In this paper, we present a nasolabial wrinkle line detecting effective algorithm based on the Active appearance model and Hessian filter to improve localization results by creating unique initial shapes of the wrinkle lines for each input face image.

Results

Experimental results show that the proposed method is capable of tracking curve wrinkle lines, thus allowing to detect complexly structured wrinkle lines. This work demonstrates results illustrated the competitiveness of the proposed method in detecting nasolabial wrinkle lines.

Conclusion

In our study, this was introduced the effectiveness of changing the structure of AAM and successfully applied in wrinkle line localizing, although competitive results are achieved by the proposed wrinkle detection method.

Image Deblurring Using Multi-Stream Bottom-Top-Bottom Attention Network and Global Information-Based Fusion and Reconstruction Network

Image deblurring has been a challenging ill-posed problem in computer vision. Gaussian blur is a common model for image and signal degradation. The deep learning-based deblurring methods have attracted much attention due to their advantages over the traditional methods relying on hand-designed features. However, the existing deep learning-based deblurring techniques still cannot perform well in restoring the fine details and reconstructing the sharp edges. To address this issue, we have designed an effective end-to-end deep learning-based non-blind image deblurring algorithm. In the proposed method, a multi-stream bottom-top-bottom attention network (MBANet) with the encoder-to-decoder structure is designed to integrate low-level cues and high-level semantic information, which can facilitate extracting image features more effectively and improve the computational efficiency of the network. Moreover, the MBANet adopts a coarse-to-fine multi-scale strategy to process the input images to improve image deblurring performance. Furthermore, the global information-based fusion and reconstruction network is proposed to fuse multi-scale output maps to improve the global spatial information and recurrently refine the output deblurred image. The experiments were done on the public GoPro dataset and the realistic and dynamic scenes (REDS) dataset to evaluate the effectiveness and robustness of the proposed method. The experimental results show that the proposed method generally outperforms some traditional deburring methods and deep learning-based state-of-the-art deblurring methods such as scale-recurrent network (SRN) and denoising prior driven deep neural network (DPDNN) in terms of such quantitative indexes as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) and human vision.

Training Very Deep CNNs for General Non-Blind Deconvolution

Non-blind image deconvolution is an ill-posed problem. The presence of noise and band-limited blur kernels makes the solution of this problem non-unique. Existing deconvolution techniques produce a residual between the sharp image and the estimation that is highly correlated with the sharp image, the kernel, and the noise. In most cases, different restoration models must be constructed for different blur kernels and different levels of noise, resulting in low computational efficiency or highly redundant model parameters. Here we aim to develop a single model that handles different types of kernels and different levels of noise: general non-blind deconvolution. Specifically, we propose a very deep convolutional neural network that predicts the residual between a pre-deconvolved image and the sharp image rather than the sharp image. The residual learning strategy makes it easier to train a single model for different kernels and different levels of noise, encouraging high effectiveness and efficiency. Quantitative evaluations demonstrate the practical applicability of the proposed model for different blur kernels. The model also shows state-of-the-art performance on synthesized blurry images.

Therapeutic Effects and Finite Element Analysis of a Combined Treatment Using Laser Needle-Knife with Supine Repositioning Massage on Patients with Cervical Spondylotic Vertebral Arteriopathy

Objective: To investigate the effectiveness of combined laser needle-knife and massage on cervical spondylotic vertebral arteriopathy patients. Summary of background data: With the recent rise of electronic businesses, the incidence of cervical spondylosis has also risen rapidly. Methods: Cervical spondylotic vertebral arteriopathy patients were treated using laser needle-knife with massage, and compared to patients who only received the massage. A 3D anatomical and hemodynamic model was developed. Results: The symptomatic and functional overall scores were reduced by 71.43% after the combined treatment. Results from the finite element analysis indicated that the maximum flow rate of the left vertebral artery was improved by 47.52% and the right was improved by 38.67%. Conclusion: A combined treatment of cervical spondylotic vertebral arteriopathy using laser needle-knife and massage is an effective approach with a therapeutic mechanism closely related to hemodynamics.