White Images Research Articles

Challenges of brain white matter imaging: proceed with caution.

aCentre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Toronto, ON, Canada bUniversity of Toronto Centre for the Study of Pain, Toronto, ON, Canada cKrembil Research Institute, University Health Network, Toronto, ON, Canada dDepartment of Dentistry, Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada *E-mail address: [email protected] (M. Moayedi) Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

SHCF: A supervised hierarchical clustering approach to remove high density salt and pepper noise from black and white content digital images

SHCF: A supervised hierarchical clustering approach to remove high density salt and pepper noise from black and white content digital images

Changes in group behaviour in response to a preferred environment reflect positive affect

When observed in their preferred environments, animals display behavioural changes, such as an increase in resting or a reduction in agonism, suggestive of positive affect and improved welfare. However, most studies focus on the behaviour of individuals or, at most, pairs of animals; even though in group-living animals beneficial environmental changes may impact on how the group behaves as a whole. In this study, we investigated whether experiencing a preferred visual environment affected the shoaling behaviour of zebrafish (Danio rerio) groups. We first confirmed a group preference for an image of gravel placed underneath the base of a tank compared to a plain white image. Second, we observed replicated groups either with or without the preferred (gravel) image present to determine if a visually enriched and preferred environment could elicit changes in shoaling behaviour. We found a significant interaction between the observation time and test condition, with differences in shoaling behaviour reflective of increased relaxation emerging gradually over time in the gravel condition. The findings of this study reveal that experiencing a preferred environment can alter group behaviour, making such holistic changes valuable as potential indicators of positive welfare.

Efficient large invisible color watermark embedding using conditional deep autoencoder model for medical applications

The widespread availability of the internet, in conjunction with very low-cost digital recording and storage devices, has ushered in an age in which the reproduction, illegal use, and malicious dissemination of digital information have become much more straightforward. Authentication of multimedia materials has garnered a lot of interest in recent days as a means of preventing illegal usage, theft, and misrepresentation of the content. Invisible watermarking tries to hide information in a medium in order to demonstrate ownership, integrity, or to hide a secret message. The goal of invisible watermarking is to hide the watermark and extract it without making it evident that the cover image is watermarked. This paper presents an invisible watermarking technique that can hide large color water marks in the cover media. The proposed model uses a Conditional Variational Autoencoder (CVAE) to encode the watermark into the cover image. The watermarked image is then decoded at the receiver to extract the watermark. Unlike the conventional watermarking techniques which hide a simple, small black and white image as watermark, the proposed model can embed large full color images as watermarks into the cover images. This makes the proposed model superior to the existing models by hiding large watermarks into the color images. The stego images produced a high PSNR greater than 40 for different watermarks and the images are visually indistinct from the original cover images.

Deep learning for few-shot white blood cell image classification and feature learning

ABSTRACT Differential counting of white blood cells (WBCs) in bone marrow using artificial intelligence (AI)-based models, such as convolutional neural network (CNN) and its various variants, can help physicians to efficiently diagnose many critical diseases such as leukaemia, AIDS and cancers. In this work, we implement a deep transfer learning to several CNN models to examine their effectiveness on automatically classifying WBCs into lymphocytes and non-lymphocytes groups. Our results show that transfer learning can enhance the training of the model and improve the model performance. We also discover that using image masking to remove irrelevant image pixels can further increase the accuracy of the model predictions. Moreover, we assess the impact of three data augmentation techniques to address the imbalance in the data set, which commonly occurs in many biological applications. Our results show that all the three examined data augmentation methods improve the classification results on both training and testing data sets. Altogether, we demonstrate that deep neural networks, when combined with transfer learning and imaging processing techniques, can serve as a powerful tool to conduct automatic differential counting of WBCs, and thus facilitate the diagnosis of the WBC-related disorders, monitor the disease progression and improve the effectiveness of therapeutics.

Hematologic Cancer Detection Using White Blood Cancerous Cells Empowered with Transfer Learning and Image Processing.

Lymphoma and leukemia are fatal syndromes of cancer that cause other diseases and affect all types of age groups including male and female, and disastrous and fatal blood cancer causes an increased savvier death ratio. Both lymphoma and leukemia are associated with the damage and rise of immature lymphocytes, monocytes, neutrophils, and eosinophil cells. So, in the health sector, the early prediction and treatment of blood cancer is a major issue for survival rates. Nowadays, there are various manual techniques to analyze and predict blood cancer using the microscopic medical reports of white blood cell images, which is very steady for prediction and causes a major ratio of deaths. Manual prediction and analysis of eosinophils, lymphocytes, monocytes, and neutrophils are very difficult and time-consuming. In previous studies, they used numerous deep learning and machine learning techniques to predict blood cancer, but there are still some limitations in these studies. So, in this article, we propose a model of deep learning empowered with transfer learning and indulge in image processing techniques to improve the prediction results. The proposed transfer learning model empowered with image processing incorporates different levels of prediction, analysis, and learning procedures and employs different learning criteria like learning rate and epochs. The proposed model used numerous transfer learning models with varying parameters for each model and cloud techniques to choose the best prediction model, and the proposed model used an extensive set of performance techniques and procedures to predict the white blood cells which cause cancer to incorporate image processing techniques. So, after extensive procedures of AlexNet, MobileNet, and ResNet with both image processing and without image processing techniques with numerous learning criteria, the stochastic gradient descent momentum incorporated with AlexNet is outperformed with the highest prediction accuracy of 97.3% and the misclassification rate is 2.7% with image processing technique. The proposed model gives good results and can be applied for smart diagnosing of blood cancer using eosinophils, lymphocytes, monocytes, and neutrophils.

Object identification in cerebral visual impairment characterized by gaze behavior and image saliency analysis

Individuals with cerebral visual impairment (CVI) have difficulties identifying common objects, especially when presented as cartoons or abstract images. In this study, participants were shown a series of images of ten common objects, each from five possible categories ranging from abstract black & white line drawings to color photographs. Fifty individuals with CVI and 50 neurotypical controls verbally identified each object and success rates and reaction times were collected. Visual gaze behavior was recorded using an eye tracker to quantify the extent of visual search area explored and number of fixations. A receiver operating characteristic (ROC) analysis was also carried out to compare the degree of alignment between the distribution of individual eye gaze patterns and image saliency features computed by the graph-based visual saliency (GBVS) model. Compared to controls, CVI participants showed significantly lower success rates and longer reaction times when identifying objects. In the CVI group, success rate improved moving from abstract black & white images to color photographs, suggesting that object form (as defined by outlines and contours) and color are important cues for correct identification. Eye tracking data revealed that the CVI group showed significantly greater visual search areas and number of fixations per image, and the distribution of eye gaze patterns in the CVI group was less aligned with the high saliency features of the image compared to controls. These results have important implications in helping to understand the complex profile of visual perceptual difficulties associated with CVI.

Encryption and Decryption of Images with Pixel Data Modification Using Hand Gesture Passcodes

To ensure data security and safeguard sensitive information in society, image encryption and decryption as well as pixel data modifications, are essential. To avoid misuse and preserve trust in our digital environment, it is crucial to use these technologies responsibly and ethically. So, to overcome some of the issues, the authors designed a way to modify pixel data that would hold the hidden information. The objective of this work is to change the pixel values in a way that can be used to store information about black and white image pixel data. Prior to encryption and decryption, by using Python we were able to construct a passcode with hand gestures in the air, then encrypt it without any data loss. It concentrates on keeping track of simply two pixel values. Thus, pixel values are slightly changed to ensure the masked image is not misleading. Considering that the RGB values are at their border values of 254, 255 the test cases of masking overcome issues with the corner values susceptibility.

The Effectiveness of Data Augmentation for Mature White Blood Cell Image Classification in Deep Learning — Selection of an Optimal Technique for Hematological Morphology Recognition —

The Effectiveness of Data Augmentation for Mature White Blood Cell Image Classification in Deep Learning — Selection of an Optimal Technique for Hematological Morphology Recognition —

Basal Ganglia Compensatory White Matter Changes on DTI in Alzheimer's Disease.

The volume reduction of the gray matter structures in patients with Alzheimer's disease is often accompanied by an asymmetric increase in the number of white matter fibers located close to these structures. The present study aims to investigate the white matter structure changes in the motor basal ganglia in Alzheimer's disease patients compared to healthy controls using diffusion tensor imaging. The amounts of tracts, tract length, tract volume, quantitative anisotropy, and general fractional anisotropy were measured in ten patients with Alzheimer's disease and ten healthy controls. A significant decrease in the number of tracts and general fractional anisotropy was found in patients with Alzheimer's disease compared to controls in the right caudate nucleus, while an increase was found in the left and the right putamen. Further, a significant decrease in the structural volume of the left and the right putamen was observed. An increase in the white matter diffusion tensor imaging parameters in patients with Alzheimer's disease was observed only in the putamen bilaterally. The right caudate showed a decrease in both the diffusion tensor imaging parameters and the volume in Alzheimer's disease patients. The right pallidum showed an increase in the diffusion tensor imaging parameters but a decrease in volume in Alzheimer's disease patients.

Single-Benzene Dual-Emitters Harness Excited-State Antiaromaticity for White Light Generation and Fluorescence Imaging.

Molecular emitters simultaneously generating light at different wavelengths have wide applications. With a small molecule, however, it is challenging to realize two independent radiative pathways. We invented the first examples of dual-emissive single-benzene fluorophores (SBFs). Two emissive tautomers are generated by synthetic modulation of the hydrogen bond acidity, which opens up pathways for excited-state proton transfer. White light is produced by a delicate balance between the energy and intensity of the emission from each tautomer. We show that the excited-state antiaromaticity of the benzene core itself dictates the proton movements driving the tautomer equilibrium. Using this simple benzene platform, a fluorinated SBF was synthesized with a record high solubility in perfluorocarbon solvents. White light-emitting devices and multicolor imaging of perfluorocarbon nanodroplets in live cells demonstrate the practical utility of these molecules.

Digital soil mapping using drone images and machine learning at the sloping vegetable fields in cool highland in the Northern Kanto region, Japan

ABSTRACT In the cool highlands agricultural area in the Kanto region in Japan, large-scale vegetable cultivation is taking place in sloping fields where Andosols are distributed. In some steeply sloping fields in the area, soil erosion has resulted in the loss of surface soil and its redeposition, causing heterogeneity of soil productivity. In this study, a high-resolution soil map (1 m resolution) was delineated using drone images and machine learning to understand the status of soil productivity in sloping vegetable fields. A digital elevation model (DEM) and orthoimages were created from the analysis of images taken by a drone. Then, 13 topographic index maps, such as slopes, were created from the DEM. The orthoimages were then converted to black and white images to quantify surface soil color. Based on the black and white images and topographic indices of the field, the distribution map of 1) organic carbon content of surface soil and 2) layer thickness of A horizon in the study area were delineated by the Regression-Kriging method. The Empirical Bayesian Kriging method was used to delineate maps of 3) gravel content in the soil profile (0–60 cm) and 4) depth to the gravel layer. Using the 13 topographic index maps and the maps from 1) to 4) as features, a predicted soil map was delineated using the random forest method with eight soil series groups as the map unit. Nine features were selected by the best-predicted model. High-Humic Cumulic Allophanic Andosols and Skeletal Cumulic Allophanic Andosols were generally covered on gentle slopes with low LS-Factor, which was calculated by slope and specific catchment area. Skeletal Low-humic Allophanic Andosols were mainly distributed on steep slopes with high LS-Factor and susceptible to soil erosion. A high-resolution soil map reflecting soil erosion was able to delineate using drone images and machine learning in Andosol's sloping upland field.

An IoT-based smart optical platform for colorimetric analyzing multiple samples of biomarkers

In this work, we have designed, implemented, and characterized an IoT-enabled optical platform that serves as a highly efficient colorimetric analyzer for disposable rapid diagnostic test kits. We have taken color images of the test chip using an external camera module connected to an IoT-enabled single-board mini-computer, which are then analyzed using an image processing algorithm. To prevent image burning and to reduce the impact of ambient lighting, we have proposed a unique light-diffusing model and have maintained a strategic distance within the white acrylic imaging enclosure of the platform. In order to ensure even and uniform distribution of light on the sample within the platform, we have carried out a study with the incorporation of a highly bright single LED, an LED array, and attaching a PDMS-made light diffuser on the LED array at varying light incident angles on custom-made single-channel test kits containing blood hematocrit. Finally, based on the optimized lighting conditions, we have successfully applied the proposed platform for the detection of blood hematocrit, β-hCG, and SARS-CoV-2 antigen, and were able to identify their different levels of concentration. Since the device is portable in size and cost-effective to implement, it can also be used for analyzing other biomarkers in resource-limited communities for point-of-care (POC) applications.

An analysis of the microstructure of white matter in migrain: a pilot study

White matter microstructure imaging has long been studied and analyzed with tensor diffusion pulse sequences. White matter integrity analysis has been performed on many different groups of cranial neuropathies. Migraine is a typical unilateral pulsating headache disorder. Although the prevalence of migraine is very high, the pathophysiology is still unclear. From the hypothesis, migraine and persistent migraine may affect the white matter microstructure image. Based on the diffusion of water molecules in the white matter nerve fiber bundle, and the connection to the “pain matrix”. Tract-based spatial statistics (TBSS) can demonstrate whether or not there is a difference in the water diffusion characteristics of fiber bundles in migraine patients compared with healthy volunteers. We conducted a pilot study to understand the TBSS analysis and white matter microstructure analysis in patients diagnosed with migraine. Key words: migrain, microstructures, white matter intergrity, DTI, TBSS

Two new Nolini (Lepidoptera: Nolidae: Nolinae) from Nouabalé-Ndoki National Park, Republic of Congo

A new species of Bellanola László, 2022 (B. ndoki László, sp. n.) and a new species of Meganola Dyar, 1898 (M. faber László, sp. n.) are described and figured from the Nouabalé-Ndoki National Park, Republic of Congo. The former new species is the second known taxon of the recently described genus Bellanola, whilst the latter is attributed to the Meganola chionea (Hampson, 1911) species-group. The paper is illustrated with habitat photographs and eight colour and eight black and white images of diagnostic features.

Create the best first glance: The cross-cultural effect of image background on purchase intention

As globalization drives more firms toward cross-border e-commerce (CBEC), a well-designed decision support system becomes crucial to gain a competitive edge in the international market. Product images, a vital aspect of the system interface, play a significant role in shaping users' first impressions, facilitating seller-buyer information interaction, and ultimately enhancing users' decisions making in the system. Across a series of studies, this research investigates the effect of cultural differences (thinking style: holistic vs. analytic) on image background and reveals the underlying mechanism. Results show that online consumers from cultures characterized by holistic thinking style (Chinese sample) are more prone to purchase products presented with contextual backgrounds than those with white backgrounds, while this effect is absent for online consumers from cultures that tend to think in an analytic way (American sample). This effect is also observed when the thinking style is primed within the culture in separate samples from the United States and China. Study 3 employs eye-tracking technology and shows that holistic thinking, compared to analytic thinking, results in an asymmetry in cognitive effort to purchase the same products framed with contextual and white background images. Specifically, contextual (vs. white) background information greatly assists holistic thinking consumers in understanding the product, enabling them to spend less cognitive effort on product information processing. Instead, the cognitive effort that analytic thinking consumers spare in the product information is not affected by the background. Finally, we discuss theoretical contributions and practical insights for CBEC retailers and system designers that the findings indicate.

Influence of Skin Color and Data Pre-processing on Gender Classification

Face recognition and gender classification is one of the hot spots at present, how to get better results in the recognition process is the key. This research was based on neural networks. While some researchers have explored this issue through neural networks, this research focuses on the impact of data processing other than the model on the final results. First of all, through the manual review and screening of data sets, the training set of face images only retained deep color skin face images, and the test set did not do selection processing. The final results of the model which based on selected data set are not much different from that of the model based on the unselected training set, except for a slight decrease in the recall rate of female face recognition. Secondly, by the method of control variables, the effect of the three kinds of data pre-processing is compared. The results show that both random cropping and normalization can improve some attributes of the model to varying degrees, but only random horizontal flipping has a certain negative effect on the model. In this research, it was found that the smaller proportion of white skin face image training sets hardly affected the model performance. Data pre-processing can effectively improve the model, but the use of random horizontal flips in a data set with a great number of small tilt angle images may lead to the deterioration of the results on the training set.

Explainable AI identifies diagnostic cells of genetic AML subtypes.

Explainable AI is deemed essential for clinical applications as it allows rationalizing model predictions, helping to build trust between clinicians and automated decision support tools. We developed an inherently explainable AI model for the classification of acute myeloid leukemia subtypes from blood smears and found that high-attention cells identified by the model coincide with those labeled as diagnostically relevant by human experts. Based on over 80,000 single white blood cell images from digitized blood smears of 129 patients diagnosed with one of four WHO-defined genetic AML subtypes and 60 healthy controls, we trained SCEMILA, a single-cell based explainable multiple instance learning algorithm. SCEMILA could perfectly discriminate between AML patients and healthy controls and detected the APL subtype with an F1 score of 0.86±0.05 (mean±s.d., 5-fold cross-validation). Analyzing a novel multi-attention module, we confirmed that our algorithm focused with high concordance on the same AML-specific cells as human experts do. Applied to classify single cells, it is able to highlight subtype specific cells and deconvolve the composition of a patient's blood smear without the need of single-cell annotation of the training data. Our large AML genetic subtype dataset is publicly available, and an interactive online tool facilitates the exploration of data and predictions. SCEMILA enables a comparison of algorithmic and expert decision criteria and can present a detailed analysis of individual patient data, paving the way to deploy AI in the routine diagnostics for identifying hematopoietic neoplasms.

Real‐time coloring method of laser surgery video based on generative adversarial network

AbstractLasers have become an important technology for the medical treatment of cancer. However, the high power of visible laser knives makes conventional endoscopic imaging light pollution severe. This phenomenon can cause the physician to be unable to determine the condition of the lesion site. In this article, a colorization method for laser surgery videos is proposed. The method performs real‐time colorization of the laser surgery video under black‐and‐white imaging. The main contribution of this work is to propose a new idea for solving laser contamination in medical surgery videos, along with real‐time generative adversarial networks (RTGANs). The network takes advantage of generative adversarial networks and residual networks for fast colorization of black and white images. To evaluate the method, we conducted experiments using three datasets. The experimental results show that the RTGAN network takes only 0.026 s to colorize a single frame image. Coloring speed up to 67 times faster than advanced methods, and the Peak Signal to Noise Ratio reached a maximum of 35.736.

Tracking Nonradial Outflows in Extreme Ultraviolet and White Light Solar Images

Understanding the solar corona requires knowledge of its dynamics through its various layers and subsequent connectivity to the heliosphere. This requires understanding the nature of the outflows and the physical transitions through the middle corona (∼1.5–6.0 R s ). While this region is still inaccessible to in situ measurements, remote sensing observations are available, but their interpretation can be controversial due to line-of-sight effects and the nonradial motion of outflowing structures close to the Sun (≤3.0 R s ). In this work, we describe a method to mitigate these challenges by generating nonradial height–time profiles of outflows by using advanced image processing techniques. The northern and southern boundaries of a large equatorial streamer during the 2008 solar minimum were identified in STEREO/SECCHI solar images, using two different methodologies based on thresholds of brightness and piecewise polynomial function fitting. To address line-of-sight issues, we used tomographic reconstruction of the 3D distribution of the coronal electron density based on SECCHI/COR2 images. Spectral analysis of the time series of the position angle of the streamer boundary revealed its oscillatory nature at some heights at 36–48 and 10.5–14.6 hr. Dividing the distance between the northern and southern streamer boundaries in equal parts at each height, we obtained nonradial height–time paths from which we generated nonradial profiles of corona/solar wind plasma outflow. We tracked outflows as they moved uninterruptedly from the Sun in EUVI, through COR1 and into COR2. Finally, we discuss the preliminary results of nonradial plane-of-sky velocities for a coronal mass ejection and two small-scale features.