Set Of Images Research Articles

Development of a mobile, high-throughput, and low-cost image-based plant growth phenotyping system.

Nondestructive plant phenotyping forms a key technique for unraveling molecular processes underlying plant development and response to the environment. While the emergence of high-throughput phenotyping facilities can further our understanding of plant development and stress responses, their high costs greatly hinder scientific progress. To democratize high-throughput plant phenotyping, we developed sets of low-cost image- and weight-based devices to monitor plant shoot growth and evapotranspiration. We paired these devices to a suite of computational pipelines for integrated and straightforward data analysis. The developed tools were validated for their suitability for large genetic screens by evaluating a cowpea (Vigna unguiculata) diversity panel for responses to drought stress. The observed natural variation was used as an input for a genome-wide association study, from which we identified nine genetic loci that might contribute to cowpea drought resilience during early vegetative development. The homologs of the candidate genes were identified in Arabidopsis (Arabidopsis thaliana) and subsequently evaluated for their involvement in drought stress by using available T-DNA insertion mutant lines. These results demonstrate the varied applicability of this low-cost phenotyping system. In the future, we foresee these setups facilitating the identification of genetic components of growth, plant architecture, and stress tolerance across a wide variety of plant species.

On the relationship between viscoelasticity and water diffusion in soft biological tissues

Magnetic resonance elastography (MRE) and diffusion-weighted imaging (DWI) are complementary imaging techniques that detect disease based on viscoelasticity and water mobility, respectively. However, the relationship between viscoelasticity and water diffusion is still poorly understood, hindering the clinical translation of combined DWI-MRE markers.We used DWI-MRE to study 129 biomaterial samples including native and cross-linked collagen, glycosaminoglycans (GAGs) with different sulfation levels, and decellularized specimens of pancreas and liver, all with different proportions of solid tissue, or solid fractions. We developed a theoretical framework of the relationship between mechanical loss and tissue-water mobility based on two parameters, solid and fluid viscosity. These parameters revealed distinct DWI-MRE property clusters characterizing weak, moderate, and strong water-network interactions. Sparse networks interacting weakly with water, such as collagen or diluted decellularized tissue, resulted in marginal changes in water diffusion over increasing solid viscosity. In contrast, dense networks with larger solid fractions exhibited both free and hindered water diffusion depending on the polarity of the solid components. For example, polar and highly sulfated GAGs as well as native soft tissues hindered water diffusion despite relatively low solid viscosity.Our results suggest that two fundamental properties of tissue networks, solid fraction and network polarity, critically influence solid and fluid viscosity in biological tissues. Since clinical DWI and MRE are sensitive to these viscosity parameters, the framework we present here can be used to detect tissue remodeling and architectural changes in the setting of diagnostic imaging. Statement of SignificanceThe viscoelastic properties of biological tissues provide a wealth of information on the vital state of cells and host matrix. Combined measurement of viscoelasticity and water diffusion by medical imaging is sensitive to tissue microarchitecture. However, the relationship between viscoelasticity and water diffusion is still poorly understood, hindering full exploitation of these properties as a combined clinical biomarker. Therefore, we analyzed the parameter space accessible by diffusion-weighted imaging (DWI) and magnetic resonance elastography (MRE) and developed a theoretical framework for the relationship between water mobility and mechanical parameters in biomaterials. Our theory of solid material properties related to particle motion can be translated to clinical radiology using clinically established MRE and DWI.

The use of ferumoxytol for high-resolution vascular imaging and troubleshooting for abdominal allografts.

Ferumoxytol is an ultrasmall superparamagnetic iron oxide which has been used as an off-label intravenous contrast agent for MRI. Unlike gadolinium-based contrast agents, ferumoxytol remains in the intravascular space with a long half-life of 14-21h. During the first several hours, it acts as a blood-pool agent and has minimal parenchymal enhancement. Studies have shown adequate intravascular signal for up to 72h after initial contrast bolus. Ferumoxytol has been shown to be safe, even in patients with renal failure. Ferumoxytol has shown promise in a variety of clinical settings. The exquisite resolution enabled by the long intravascular times and lack of background parenchymal enhancement is of particular interest in the vascular imaging of solid organ allografts. Ferumoxytol magnetic resonance angiography (MRA) may identify clinically actionable findings months before ultrasound, CT angiography, or Gadolinium-enhanced MRA. Ferumoxytol MRA is of particular benefit as a troubleshooting tool in the setting of equivocal ultrasound and CT imaging. In the following review, we highlight the use of ferumoxytol for high-resolution MR vascular imaging for abdominal solid organ allografts, with representative cases.

MagiCapture: High-Resolution Multi-Concept Portrait Customization

Large-scale text-to-image models including Stable Diffusion are capable of generating high-fidelity photorealistic portrait images. There is an active research area dedicated to personalizing these models, aiming to synthesize specific subjects or styles using provided sets of reference images. However, despite the plausible results from these personalization methods, they tend to produce images that often fall short of realism and are not yet on a commercially viable level. This is particularly noticeable in portrait image generation, where any unnatural artifact in human faces is easily discernible due to our inherent human bias. To address this, we introduce MagiCapture, a personalization method for integrating subject and style concepts to generate high-resolution portrait images using just a few subject and style references. For instance, given a handful of random selfies, our fine-tuned model can generate high-quality portrait images in specific styles, such as passport or profile photos. The main challenge with this task is the absence of ground truth for the composed concepts, leading to a reduction in the quality of the final output and an identity shift of the source subject. To address these issues, we present a novel Attention Refocusing loss coupled with auxiliary priors, both of which facilitate robust learning within this weakly supervised learning setting. Our pipeline also includes additional post-processing steps to ensure the creation of highly realistic outputs. MagiCapture outperforms other baselines in both quantitative and qualitative evaluations and can also be generalized to other non-human objects.

A3C: An Image-Association-Based Computing Device Authentication Framework for People with Upper Extremity Impairments

Current computing device authentication often presents accessibility barriers for people with upper extremity impairments (UEI). In this paper, we present a framework called Accessible image-Association-based Authentication for Computing-devices (A3C) , a novel recognition-based graphical authentication framework specifically designed for people with UEI to authenticate to their computing devices. A3C requires users to provide a set of primary images that the user knows that are recognizable to them and subsequently associate each primary image with a secondary image. To evaluate the efficacy of the A3C framework, we instantiated the framework by implementing a version of A3C called A3C-FA , which uses images of faces of people the user knows as the primary image and animal images as the secondary image. We then performed three studies to evaluate A3C-FA: a shoulder-surfing attack study ( N=319 ), a close-adversary attack study ( N=268 ), and a usability study with people with UEI ( N=14 ). We found that A3C was robust against both shoulder-surfing and close-adversary attacks. We also performed a detailed study to evaluate the accessibility of A3C-FA. Our participants reported that A3C-FA was more usable and more secure than the authentication approaches with which they were familiar. Based on these findings, we suggest four areas of future research to further improve the design of A3C framework.

Nonunion scaphoid bone shape prediction using iterative kernel principal polynomial shape analysis.

The scaphoid is an important mechanical stabilizer for both the proximal and distal carpal columns. The precise estimation of the complete scaphoid bone based on partial bone geometric information is a crucial factor in the effective management of scaphoid nonunion. Statistical shape model (SSM) could be utilized to predict the complete scaphoid shape based on the defective scaphoid. However, traditional principal component analysis (PCA) based SSM is limited by its linearity and the inability to adjust the number of modes used for prediction. This study proposes an iterative kernel principal polynomial shape analysis (iKPPSA)-based SSM to predict the pre-morbid shape of the scaphoid, aiming at enhancing the accuracy as well as the robustness of the model. Sixty-five sets of scaphoid images were used to train SSM and nine sets of scaphoid images were used for validation. For each validation image set, three defect types (tubercle, proximal pole, and avascular necrosis) were virtually created. The predicted shapes of the scaphoid by PCA, PPSA, KPCA, and iKPPSA-based SSM were evaluated against the original shape in terms of mean error, Hausdorff distance error, and Dice coefficient. The proposed iKPPSA-based scaphoid SSM demonstrates significant robustness, with a generality of 0.264mm and a specificity of 0.260mm. It accounts for 99% of variability with the first seven principal modes of variation. Compared to the traditional PCA-based model, the iKPPSA-based scaphoid model prediction demonstrated superior performance for the proximal pole type fracture, with significant reductions of 25.2%, 24.7%, and 24.6% in mean error, Hausdorff distance, and root mean square error (RMSE), respectively, and a 0.35% improvement in Dice coefficient. This study showed that the iKPPSA-based SSM exploits the nonlinearity of data features and delivers high reconstruction accuracy. It can be effectively integrated into preoperative planning for scaphoid fracture management or morphology-based biomechanical modeling of the scaphoid.

Online electronic signature recognition using sparse classification techniques that support neural models

With the rapid development of information technology, electronic signature plays an increasingly important role in people’s production practice. However, there are a large number of hackers maliciously stealing information in the network. In order to avoid this phenomenon, we urgently need to strengthen the research on online electronic signature recognition technology. Based on the sparse classification technology of neural model, this paper constructs an online electronic signature recognition model by using convolutional neural network and sparse classification technology. We first extract the local features of online electronic signatures, construct feature vectors and perform sparse representation. Sub-model we construct a scheme for online electronic signature recognition based on neural models and sparse classification techniques using a combination of algorithms. We first extract the local features of online electronic signatures, construct feature vectors and perform sparse representation. At the same time, the features in the training image set are extracted, local feature sets are constructed, feature dictionaries are created, and the vectors in the feature dictionaries are matched with the global sparse vectors constructed by the electronic signatures to be detected, and the matching results are finally obtained. At the same time, the features in the training image set are extracted, the local feature set is constructed, the feature dictionary is created, and the vector in the feature dictionary is matched with the global sparse vector constructed by the electronic signature to be detected, and finally the matching result is obtained. In order to verify the accuracy of the model, we first extracted 1000 respondents for online e-signature recognition experimental results show that the recognition accuracy of online e-signature has been significantly improved. Finally, in order to determine the optimal number of training sets for the model constructed in this experiment, we analyzed the correlation between training and sample size and recognition accuracy. Finally, it was concluded that the recognition accuracy increased with the increase of the number of training samples. Electronic signatures can quickly examine the signature results, and electronic signature recognition can be used to fix and tamper-proof evidence to enhance the security and trustworthiness of signatures, and it is imperative to improve the security of electronic signatures. In this paper, we study online electronic signature recognition technology, using neural model and sparse classification to construct an efficient and accurate recognition model. Experiments show that the model is effective and the number of training samples affects the recognition accuracy. This paper provides a new approach for the development of this technique. When the training samples are greater than 1300, the recognition accuracy is stable at 95%. This research has certain theoretical and practical significance, and promotes the rapid development of online electronic signature recognition.

Automatic hepatic tumor segmentation in intra-operative ultrasound: a supervised deep-learning approach.

Training and evaluation of the performance of a supervised deep-learning model for the segmentation of hepatic tumors from intraoperative US (iUS) images, with the purpose of improving the accuracy of tumor margin assessment during liver surgeries and the detection of lesions during colorectal surgeries. In this retrospective study, a U-Net network was trained with the nnU-Net framework in different configurations for the segmentation of CRLM from iUS. The model was trained on B-mode intraoperative hepatic US images, hand-labeled by an expert clinician. The model was tested on an independent set of similar images. The average age of the study population was 61.9 ± 9.9 years. Ground truth for the test set was provided by a radiologist, and three extra delineation sets were used for the computation of inter-observer variability. The presented model achieved a DSC of 0.84 (), which is comparable to the expert human raters scores. The model segmented hypoechoic and mixed lesions more accurately (DSC of 0.89 and 0.88, respectively) than hyper- and isoechoic ones (DSC of 0.70 and 0.60, respectively) only missing isoechoic or >20mm in diameter (8% of the tumors) lesions. The inclusion of extra margins of probable tumor tissue around the lesions in the training ground truth resulted in lower DSCs of 0.75 (). The model can accurately segment hepatic tumors from iUS images and has the potential to speed up the resection margin definition during surgeries and the detection of lesion in screenings by automating iUS assessment.

18F-FDG PET/CT and radiolabeled leukocyte SPECT/CT imaging for the evaluation of cardiovascular infection in the multimodality context: ASNC Imaging Indications (ASNC I2) Series Expert Consensus Recommendations from ASNC, AATS, ACC, AHA, ASE, EANM, HRS, IDSA, SCCT, SNMMI, and STS.

This document on cardiovascular infection, including infective endocarditis, is the first in the American Society of Nuclear Cardiology Imaging Indications (ASNC I2) series to assess the role of radionuclide imaging in the multimodality context for the evaluation of complex systemic diseases with multi-societal involvement including pertinent disciplines. A rigorous modified Delphi approach was used to determine consensus clinical indications, diagnostic criteria, and an algorithmic approach to diagnosis of cardiovascular infection including infective endocarditis. Cardiovascular infection incidence is increasing and is associated with high morbidity and mortality. Current strategies based on clinical criteria and an initial echocardiographic imaging approach are effective but often insufficient in complicated cardiovascular infection. Radionuclide imaging with 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and single photon emission computed tomography/CT leukocyte scintigraphy can enhance the evaluation of suspected cardiovascular infection by increasing diagnostic accuracy, identifying extracardiac involvement, and assessing cardiac implanted device pockets, leads, and all portions of ventricular assist devices. This advanced imaging can aid in key medical and surgical considerations. Consensus diagnostic features include focal/multi-focal or diffuse heterogenous intense 18F-FDG uptake on valvular and prosthetic material, perivalvular areas, device pockets and leads, and ventricular assist device hardware persisting on non-attenuation corrected images. There are numerous clinical indications with a larger role in prosthetic valves, and cardiac devices particularly with possible infective endocarditis or in the setting of prior equivocal or non-diagnostic imaging. Illustrative cases incorporating these consensus recommendations provide additional clarification. Future research is necessary to refine application of these advanced imaging tools for surgical planning, to identify treatment response, and more.

An improved nonlinear anisotropic model with p(x)‐growth conditions applied to image restoration and enhancement

This work proposes a novel nonlinear parabolic equation with ‐growth conditions for image restoration and enhancement. Based on the generalized Lebesgue and Sobolev spaces with variable exponent, we demonstrate the well‐posedness of the proposed model. As a first result, we prove the existence of a weak solution to our model when the reaction term is bounded by a suitable function. Secondly, we use the approximations method to establish the existence of a nonnegative weak SOLA (Solution Obtained as Limit of Approximations) solution to the proposed model. We illustrate our theoretical results in the context of image denoising and enhancement. Specifically, we present various numerical implementations on a set of grayscale images. To further enrich these simulations, we assess the efficiency of the proposed model on several color images. The obtained numerical results strongly suggest that our model outperforms existing state‐of‐the‐art methods in terms of both visual and quantitative comparison, demonstrating superior efficiency and robustness in noise removal and contrast enhancement.

Evaluation of Effectiveness of Self-Supervised Learning in Chest X-Ray Imaging to Reduce Annotated Images.

A significant challenge in machine learning-based medical image analysis is the scarcity of medical images. Obtaining a large number of labeled medical images is difficult because annotating medical images is a time-consuming process that requires specialized knowledge. In addition, inappropriate annotation processes can increase model bias. Self-supervised learning (SSL) is a type of unsupervised learning method that extracts image representations. Thus, SSL can be an effective method to reduce the number of labeled images. In this study, we investigated the feasibility of reducing the number of labeled images in a limited set of unlabeled medical images. The unlabeled chest X-ray (CXR) images were pretrained using the SimCLR framework, and then the representations were fine-tuned as supervised learning for the target task. A total of 2000 task-specific CXR images were used to perform binary classification of coronavirus disease 2019 (COVID-19) and normal cases. The results demonstrate that the performance of pretraining on task-specific unlabeled CXR images can be maintained when the number of labeled CXR images is reduced by approximately 40%. In addition, the performance was significantly better than that obtained without pretraining. In contrast, a large number of pretrained unlabeled images are required to maintain performance regardless of task specificity among a small number of labeled CXR images. In summary, to reduce the number of labeled images using SimCLR, we must consider both the number of images and the task-specific characteristics of the target images.

Few‐shot learning for satellite characterisation from synthetic inverse synthetic aperture radar images

AbstractSpace situational awareness systems primarily focus on detecting and tracking space objects, providing crucial positional data. However, understanding the complex space domain requires characterising satellites, often involving estimation of bus and solar panel sizes. While inverse synthetic aperture radar allows satellite visualisation, developing deep learning models for substructure segmentation in inverse synthetic aperture radar images is challenging due to the high costs and hardware requirements. The authors present a framework addressing the scarcity of inverse synthetic aperture radar data through synthetic training data. The authors approach utilises a few‐shot domain adaptation technique, leveraging thousands of rapidly simulated low‐fidelity inverse synthetic aperture radar images and a small set of inverse synthetic aperture radar images from the target domain. The authors validate their framework by simulating a real‐case scenario, fine‐tuning a deep learning‐based segmentation model using four inverse synthetic aperture radar images generated through the backprojection algorithm from simulated raw radar data (simulated at the analogue‐to‐digital converter level) as the target domain. The authors results demonstrate the effectiveness of the proposed framework, significantly improving inverse synthetic aperture radar image segmentation across diverse domains. This enhancement enables accurate characterisation of satellite bus and solar panel sizes as well as their orientation, even when the images are sourced from different domains.

Noise level estimation using locality preserving natural image statistics

Natural images are known to have certain regular statistical properties. These properties get modified under any artificial change or distortion in natural images. Most common form of image degradation occurs in the form of noise. The amount of degradation in noisy images is measured by estimating the noise level. Many image processing applications such as denoising, restoration, segmentation, compression etc. use noise level information as a prior; inaccurate estimate of which may impact their performance. In this article, we explore natural image statistics in locality preserving transform domain. This property groups structurally similar images/image patches when projected in the transform domain. Image patches corrupted with similar noise level get projected close by in the locality preserving domain and show consistent coefficient behaviour. In particular, we use Two Dimensional Orthogonal Locality Preserving Projection (2DOLPP) as the domain transformation technique. 2DOLPP basis, representing natural images, are learnt in advance from a set of clean images, thereby reducing the computational time significantly. Features based on natural image statistics are extracted from 2DOLPP domain representation of input image patches. Mapping from feature space to noise level is carried out using support vector regression. The proposed noise estimation approach is at par with or surpasses the state-of-the-art techniques with much less computational time. Performance of this approach is stable across a wide range of noise levels and independent of the image structure.

Deep Learning Imaging Reconstruction Algorithm for Carotid Dual Energy CT Angiography: Opportunistic Evaluation of Cervical Intervertebral Discs-A Preliminary Study.

Thus, the aim of this study is to evaluate the performance of deep learning imaging reconstruction (DLIR) algorithm in different image sets derived from carotid dual-energy computed tomography angiography (DECTA) for evaluating cervical intervertebral discs (IVDs) and compare them with those reconstructed using adaptive statistical iterative reconstruction-Veo (ASiR-V). Forty-two patients who underwent carotid DECTA were included in this retrospective analysis. Three types of image sets (70keV, water-iodine, and water-calcium) were reconstructed using 50% ASiR-V and DLIR at medium and high levels (DLIR-M and DLIR-H). The diagnostic acceptability and conspicuity of IVDs were assessed using a 5-point scale. Hounsfield Units (HU) and water concentration (WC) values of the IVDs; standard deviation (SD); and coefficient of variation (CV) were calculated. Measurement parameters of the 50% ASIR-V, DLIR-M, and DLIR-H groups were compared. The DLIR-H group showed higher scores for diagnostic acceptability and conspicuity, as well as lower SD values for HU and WC than the ASiR-V and DLIR-M groups for the 70keV and water-iodine image sets (all p < .001). However, there was no significant difference in scores and SD among the three groups for the water-calcium image set (all p > .005). The water-calcium image set showed better diagnostic accuracy for evaluating IVDs compared to the other image sets. The inter-rater agreement using ASiR-V, DLIR-M, and DLIR-H was good for the 70keV image set, excellent for the water-iodine and water-calcium image sets. DLIR improved the visualization of IVDs in the 70keV and water-iodine image sets. However, its improvement on color-coded water-calcium image set was limited.

That’s BAD: blind anomaly detection by implicit local feature clustering

Recent studies on visual anomaly detection (AD) of industrial objects/textures have achieved quite good performance. They consider an unsupervised setting, specifically the one-class setting, in which we assume the availability of a set of normal (i.e., anomaly-free) images for training. In this paper, we consider a more challenging scenario of unsupervised AD, in which we detect anomalies in a given set of images that might contain both normal and anomalous samples. The setting does not assume the availability of known normal data and thus is completely free from human annotation, which differs from the standard AD considered in recent studies. For clarity, we call the setting blind anomaly detection (BAD). We show that BAD can be converted into a local outlier detection problem and propose a novel method named PatchCluster that can accurately detect image- and pixel-level anomalies. Experimental results show that PatchCluster shows a promising performance without the knowledge of normal data, even comparable to the SOTA methods applied in the one-class setting needing it.

A Deep Learning Approach to Classification Pneumonia in Thorax Images

An algorithm of automatic learning was developed. That is abke to identify radiographic images with a pneumonia and not pneumonia diagnose based on a data set published in the “Kaggle” platform by the Radiological Society of North America, from which we obtained a set of specific images with their labels that were divided by 70% to train a convolutional neuronal network model consisting of two convolutional layers for the extraction of characteristics in each image, and ends with a classification stage in the training of the model, to conclude with 74% in metrics of accuracy given by test tests with 30% of the data set.

The universal law of generalization holds for naturalistic stimuli.

Shepard's universal law of generalization is a remarkable hypothesis about how intelligent organisms should perceive similarity. In its broadest form, the universal law states that the level of perceived similarity between a pair of stimuli should decay as a concave function of their distance when embedded in an appropriate psychological space. While extensively studied, evidence in support of the universal law has relied on low-dimensional stimuli and small stimulus sets that are very different from their real-world counterparts. This is largely because pairwise comparisons-as required for similarity judgments-scale quadratically in the number of stimuli. We provide strong evidence for the universal law in a naturalistic high-dimensional regime by analyzing an existing data set of 214,200 human similarity judgments and a newly collected data set of 390,819 human generalization judgments (N = 2,406 U.S. participants) across three sets of natural images. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A robust pairing method for two-pulse particle tracking velocimetry based on coherent point drift

Particle tracking velocity has reached a high level of maturity in time-resolved measurements since the introduction and development of the Shake-The-Box algorithm. The effectiveness of this approach lies, in part, in its ability to exploit the temporal coherence of particle trajectories to reject the ghost particles while increasing the density of true particles. However, certain situations may prevent time-resolved measurements. In those cases, a Two-Pulse configuration is often the only option. This raises a challenge with regard to the capacity in separating the ghost from the true particles due to the lack of long-term trajectories. This article proposes a new approach to solve this problem using the coherent point drift (CPD) method. This method identifies a spatially coherent deformation field that models the transformation between two correlated sets of points. In the context of particle tracking velocimetry, the imposed spatial coherence of this calculation is believed to act in the same way as the temporal coherence that made Shake-The-Box successful. The CPD is governed by three parameters whose optimal values have been evaluated in the present contribution. These values were found to be weakly sensitive to the characteristics of the flow under study, ensuring that this method is robust without further tuning of the parameters. The method is then compared with the Two-Pulse implementation of Shake-The-Box (2P-STB) available in Davis 10.2. For this purpose, sets of realistic images were generated at two successive times for different configurations based of synthetically generated turbulent flows. The Iterative-Particle-Reconstruction in Davis 10.2 was then used to extract the list of particles to be processed by CPD. The comparison shows a better recall with 2P-STB than CPD, especially for large time intervals between frames, but an overall better rejection of ghost particles by CPD than 2P-STB, which was the expected benefit of this method.

First deep search of tidal tails in the Milky Way globular cluster NGC 6362

I present results of the analysis of a set of images obtained in the field of the Milky Way globular cluster NGC 6362 using the Dark Energy Camera, which is mounted in the 4.0 m Victor Blanco telescope of the Cerro-Tololo Interamerican Observatory. The cluster was selected as a science case for deep high-quality photometry because of the controversial observational findings and theoretical predictions on the existence of cluster tidal tails. The collected data allowed me to build an unprecedented deep cluster field color-magnitude diagram, from which I filtered stars to produce a stellar density map, to trace the stellar density variation as a function of the position angle for different concentric annuli centered on the cluster, and to construct a cluster stellar density radial profile. I also built a stellar density map from a synthetic color-magnitude diagram generated from a model of the stellar population distribution in the Milky Way. The entire analysis approach converged toward a relatively smooth stellar density between 1 and ∼3.8 cluster Jacobi radii, with a slight difference smaller than two times the background stellar density fluctuation between the mean stellar density of the southeastern hemisphere and that of the northwestern one, with the latter being higher. Moreover, the spatial distribution of the recently claimed tidal tail stars agrees well not only with the observed composite star field distribution, but also with the region least affected by interstellar absorption. Nevertheless, I detected a low stellar density excess around the cluster’s Jacobi radius, from which I conclude that NGC 6362 presents a thin extra tidal halo.

Trypophobia, skin disease, and the visual discomfort of natural textures

In the last decade, the behavioral sciences have described the phenomenon of trypophobia, which is the discomfort felt by some individuals when viewing images containing clusters of bumps or holes. One evolutionary hypothesis for this phenomenon is that this visual discomfort represents an adaptation which helps organisms avoid skin disease and/or ectoparasites. Even though trypophobic imagery and disease imagery are both examples of visual textures, to date there has been no comparison of the visual discomfort elicited by these two specific kinds of textures within the larger context of the visual comfort elicited by natural texture imagery more generally. In the present study, we administered the Trypophobia Questionnaire (TQ) and recorded the visual comfort ratings elicited by a large set of standard natural texture images, including several trypophobic and skin disease images. In two independent samples, we found that while all observers find skin diseases uncomfortable to view, only those scoring high on the TQ rated trypophobic imagery equally uncomfortable. Comparable effects were observed using both standard ANOVA procedures as well as linear mixed effects modeling. Comparing the ratings of both high-TQ and low-TQ participants to the standard textures, we find remarkably consistent rank-order preferences, with the most unpleasant textures (as rated by both groups) exhibiting qualitative similarities to trypophobic imagery. However, we also find that low-level image statistics which have been previously shown to affect visual comfort are poor predictors of the visual comfort elicited by natural textures, including trypophobic and disease imagery. Our results suggest that a full understanding of the visual comfort elicited by natural textures, including those arising from skin disease, will ultimately depend upon a better understanding of cortical areas specialized for the perception of surface and material properties, and how these visual regions interact with emotional brain areas to evoke appropriate behavioral responses, like disgust.