Image Database Research Articles

Development of an automated artificial intelligence-based system for urogenital schistosomiasis diagnosis using digital image analysis techniques and a robotized microscope.

Urogenital schistosomiasis is considered a Neglected Tropical Disease (NTD) by the World Health Organization (WHO). It is estimated to affect 150 million people worldwide, with a high relevance in resource-poor settings of the African continent. The gold-standard diagnosis is still direct observation of Schistosoma haematobium eggs in urine samples by optical microscopy. Novel diagnostic techniques based on digital image analysis by Artificial Intelligence (AI) tools are a suitable alternative for schistosomiasis diagnosis. Digital images of 24 urine sediment samples were acquired in non-endemic settings. S. haematobium eggs were manually labeled in digital images by laboratory professionals and used for training YOLOv5 and YOLOv8 models, which would achieve automatic detection and localization of the eggs. Urine sediment images were also employed to perform binary classification of images to detect erythrocytes/leukocytes with the MobileNetv3Large, EfficientNetv2, and NasNetLarge models. A robotized microscope system was employed to automatically move the slide through the X-Y axis and to auto-focus the sample. A total number of 1189 labels were annotated in 1017 digital images from urine sediment samples. YOLOv5x training demonstrated a 99.3% precision, 99.4% recall, 99.3% F-score, and 99.4% mAP0.5 for S. haematobium detection. NasNetLarge has an 85.6% accuracy for erythrocyte/leukocyte detection with the test dataset. Convolutional neural network training and comparison demonstrated that YOLOv5x for the detection of eggs and NasNetLarge for the binary image classification to detect erythrocytes/leukocytes were the best options for our digital image database. The development of low-cost novel diagnostic techniques based on the detection and identification of S. haematobium eggs in urine by AI tools would be a suitable alternative to conventional microscopy in non-endemic settings. This technical proof-of-principle study allows laying the basis for improving the system, and optimizing its implementation in the laboratories.

ValveVision AI - a multimodal language model for qualitative reporting of the aortic valve in echocardiography

Abstract Background The precise assessment of the aortic valve via echocardiography is critical for early detection and management of aortic valve diseases. Until recently, previous studies have examined machine learning models to estimate individual measurements and severity of aortic stenosis (AS) from echocardiographic images. These image processing algorithms, while precise in their narrow focus, fall short in mirroring the holistic and interconnected clinical judgment typical of human echocardiographers in producing a qualitative report. Large language models (LLMs), particularly image-to-text multi-modal LLMs, are a fundamental advance in the field of deep learning with implications for a host of applications in medical imaging. They promise to encapsulate not just discrete data points typical in traditional machine learning, but also the complex contextual interrelations in clinical diagnosis. Methods In this study, a large-scale heterogeneous database of echocardiographic images containing over 90,681 studies with textual descriptors of the aortic valve was used to train a single, image-to-text multimodal LLM called ValveVision AI. The ground truth textual summaries were drafted by level III echocardiographers in a clinical setting between 2015-2020. BLEU and ROUGE score was calculated. The models were retrospectively assessed on a holdout dataset. Reviewing physicians compared the generated summary to the ground truth and binarily agreed or rejected it. Receiver Operating Characteristics (ROC) for distinct pathologies were also assessed (Figure I). Results ValveVision AI performed with a BLEU score of 0.45 and a ROUGE score of 0.49. The performance of the model in reporting on classification of moderate/severe vs none/mild AS in concordance with the validation protocol described above achieved a specificity of 91.98% and a sensitivity of 83.89%, along with more precise qualitative description. Qualitatively, the model exhibited the capability of zero-shot learning in certain instances, however, this result remains an area of exploration. Conclusion This study represents to our knowledge, the first attempt at an image-representation to text-tokenizer deep learning model architecture to mimic the thought and subtlety of echocardiographic qualitative analysis of the aortic valve. The results suggest that this multimodal LLM has sufficient accuracy to create a preliminary textual summary of the aortic valve that, if paired with a point of care ultrasound (POCUS) device in a primary care setting, may facilitate case triage, increase efficiency, and determine a more precise care pathway for patients.

Left ventricular apical aneurysms and outcomes in hypertrophic cardiomyopathy

Abstract Background/Introduction The presence of left ventricular apical aneurysm (LVAA) has been shown to be associated with increased risk of adverse events in patients with hypertrophic cardiomyopathy (HCM). However, the between LVAA size and outcomes is uncertain. Purpose To explore associations between baseline LVAA diameter and progressive changes in LVAA size over time and poor outcomes in HCM patients. Methods Our HCM multimodality imaging database (transthoracic echocardiography (TTE) or cardiac magnetic resonance (CMR)) was retrospectively assessed for studies reporting LVAA, defined as one or more akinetic or dyskinetic apical segments. LVAA size (widest systolic diameter in millimetres in the transverse plane) was recorded at two time points: earliest identification and most recent imaging, with LVAA size progression (mm/year) calculated. Patients’ electronic records were reviewed for composite outcome including stroke, apical thrombus, death, and appropriate therapy from implantable cardioverter defibrillator (ICD). Continuous variables are presented as mean ± standard deviation (SD), categorical data numerically or as percentages. Comparisons between groups were made using Wilcoxon ranks sum and Fischer exact test for continuous and binary variables, respectively. Cox regression was used to assess independent associations with composite outcomes. A p value of <0.05 was considered statistically significant. Results A total of 106 patients were identified with LVAA and clinical follow-up (mean age 64.7±11.4 years, 70% male). Mean follow-up duration was 6.5±3.0 years. Baseline LVAA was confirmed by CMR and TTE in 70 and 30% of cases respectively. Mean LVAA size at baseline was 16.2±7.6 mm (range 6-56 mm). During multivariate Cox analysis, baseline LVAA size in mm was independently associated with the composite outcome (HR 1.04, 95% CIs 1.0 to 1.08, p=0.04). Mean duration between baseline and maximal LVAA size scan was 5.4±3.0 years. Median progression in LVAA size was 1.0 (IQR 0.6 to 1.8) mm per annum (range 0.0 to 8.1 mm). During multivariate analysis, LVAA progression rate in mm per year was independently associated with the composite outcome (HR 1.4 95% CIs 1.11 to 1.7, p=0.002). Conclusion(s) These data support the association between LVAA size and poor outcomes in HCM patients. We also demonstrate the progressive nature of LVAAs and indicate those more rapidly increasing in size are at highest risk of adverse events, with potential implications for management strategies.

Improving Automatic Coronary Stenosis Classification Using a Hybrid Metaheuristic with Diversity Control

This study proposes a novel Hybrid Metaheuristic with explicit diversity control, aimed at finding an optimal feature subset by thoroughly exploring the search space to prevent premature convergence. Background/Objectives: Unlike traditional evolutionary computing techniques, which only consider the best individuals in a population, the proposed strategy also considers the worst individuals under certain conditions. In consequence, feature selection frequencies tend to be more uniform, decreasing the probability of premature convergent results and local-optima solutions. Methods: An image database containing 608 images, evenly balanced between positive and negative coronary stenosis cases, was used for experiments. A total of 473 features, including intensity, texture, and morphological types, were extracted from the image bank. A Support Vector Machine was employed to classify positive and negative stenosis cases, with Accuracy and the Jaccard Coefficient used as performance metrics. Results: The proposed strategy achieved a classification rate of 0.92 for Accuracy and 0.85 for the Jaccard Coefficient, obtaining a subset of 16 features, which represents a discrimination rate of 0.97 from the 473 initial features. Conclusions: The Hybrid Metaheuristic with explicit diversity control improved the classification performance of coronary stenosis cases compared to previous literature. Based on the achieved results, the identified feature subset demonstrates potential for use in clinical practice, particularly in decision-support information systems.

Deep Learning-Based Flap Detection System Using Thermographic Images in Plastic Surgery

In reconstructive surgery, flaps are the cornerstone for repairing tissue defects, but postoperative monitoring of their viability remains a challenge. Among the imagistic techniques for monitoring flaps, the thermal camera has demonstrated its value as an efficient indirect method that is easy to use and easy to integrate into clinical practice. This provides a narrow color spectrum image that is amenable to the development of an artificial neural network in the context of current technological progress. In the present study, we introduce a novel attention-enhanced recurrent residual U-Net (AER2U-Net) model that is able to accurately segment flaps on thermographic images. This model was trained on a uniquely generated database of thermographic images obtained by monitoring 40 patients who required flap surgery. We compared the proposed AER2U-Net with several state-of-the-art neural networks used for multi-modal segmentation of medical images, all of which are based on the U-Net architecture (U-Net, R2U-Net, AttU-Net). Experimental results demonstrate that our model (AER2U-Net) achieves significantly better performance on our unique dataset compared to these existing U-Net variants, showing an accuracy of 0.87. This deep learning-based algorithm offers a non-invasive and precise method to monitor flap vitality and detect postoperative complications early, with further refinement needed to enhance its clinical applicability and effectiveness.

Counting Cattle in Argentina with AI and Satellite Images

Abstract. From a specific need of the national institution responsible for plant and animal health in Argentina, SENASA, this collaborative work between the public sectors together with academia arises. The objective was the development of a Deep Learning algorithm for the detection of livestock, cows in particular, in very high-resolution satellite images (provided by the Argentine Space Agency (CONAE)) and its subsequent counting. The basic elements involved in the development of artificial intelligence are detailed, such as the selection and acquisition of satellite images, their very thorough preprocessing and labelling, details of the training stage and some forms of error quantification. The image database is made up of about 320 scenes (based on very high resolution (VHR) satellite data from the Pleiades and Pleiades NEO sensors, ©Airbus 2022, distributed by CONAE) of the Pampas and Patagonian regions in Argentina. Around 8,000 labels of different types of animals were generated, the most common were cows and sheep. Also labels that did not represent animals to contribute to training. Finally, some very promising preliminary results are presented, such as the average error in the count that was achieved was ± four cows. The usefulness of these tools is reflected in better management of renewable natural resources linked to animal health issues, fiscal issues and within the framework of the 2030 Agenda and Sustainable Development Goals #12, #15 and #17.

Vision Transformers for identifying asteroids interacting with secular resonances

Currently, more than 1.4 million asteroids are known in the main belt. Future surveys, like those that the Vera C. Rubin Observatory will perform, may increase this number to up to 8 million. While in the past identification of asteroids interacting with secular resonances was performed by a visual analysis of images of resonant arguments, this method is no longer feasible in the age of big data. Deep learning methods based on Convolutional Neural Networks (CNNs) have been used in the recent past to automatically classify databases of several thousands of images of resonant arguments for resonances like the ν6, the g−2g6+g5, and the s−s6−g5+g6. However, it has been shown that computer vision methods based on the Transformer architecture tend to outperform CNN models if the scale of the image database is large enough. Here, for the first time, we developed a Vision Transformer (ViT) model and applied it to publicly available databases for the three secular resonances quoted above. ViT architecture outperforms CNN models in speed and accuracy while avoiding overfitting concerns. If hyper-parameter tuning research is undertaken for each analyzed database, ViT models should be preferred over CNN architectures.

Blind Separation of Skin Chromophores from Multispectral Dermatological Images.

Background/Objectives: Based on Blind Source Separation and the use of multispectral imaging, the new approach we propose in this paper aims to improve the estimation of the concentrations of the main skin chromophores (melanin, oxyhemoglobin and deoxyhemoglobin), while considering shading as a fully-fledged source. Methods: In this paper, we demonstrate that the use of the Infra-Red spectral band, in addition to the traditional RGB spectral bands of dermatological images, allows us to model the image provided by each spectral band as a mixture of the concentrations of the three chromophores in addition to that of the shading, which are estimated through four steps using Blind Source Separation. Results: We studied the performance of our new method on a database of real multispectral dermatological images of melanoma by proposing a new quantitative performances measurement criterion based on mutual information. We then validated these performances on a database of multispectral dermatological images that we simulated using our own new protocol. Conclusions: All the results obtained demonstrated the effectiveness of our new approach for estimating the concentrations of the skin chromophores from a multispectral dermatological image, compared to traditional approaches that consist of using only the RGB image by neglecting shading.

Multi-View Soft Attention-Based Model for the Classification of Lung Cancer-Associated Disabilities.

Background: The detection of lung nodules at their early stages may significantly enhance the survival rate and prevent progression to severe disability caused by advanced lung cancer, but it often requires manual and laborious efforts for radiologists, with limited success. To alleviate it, we propose a Multi-View Soft Attention-Based Convolutional Neural Network (MVSA-CNN) model for multi-class lung nodular classifications in three stages (benign, primary, and metastatic). Methods: Initially, patches from each nodule are extracted into three different views, each fed to our model to classify the malignancy. A dataset, namely the Lung Image Database Consortium Image Database Resource Initiative (LIDC-IDRI), is used for training and testing. The 10-fold cross-validation approach was used on the database to assess the model's performance. Results: The experimental results suggest that MVSA-CNN outperforms other competing methods with 97.10% accuracy, 96.31% sensitivity, and 97.45% specificity. Conclusions: We hope the highly predictive performance of MVSA-CNN in lung nodule classification from lung Computed Tomography (CT) scans may facilitate more reliable diagnosis, thereby improving outcomes for individuals with disabilities who may experience disparities in healthcare access and quality.

Towards transforming malaria vector surveillance using VectorBrain: a novel convolutional neural network for mosquito species, sex, and abdomen status identifications

Malaria is a major public health concern, causing significant morbidity and mortality globally. Monitoring the local population density and diversity of the vectors transmitting malaria is critical to implementing targeted control strategies. However, the current manual identification of mosquitoes is a time-consuming and intensive task, posing challenges in low-resource areas like sub-Saharan Africa; in addition, existing automated identification methods lack scalability, mobile deployability, and field-test validity. To address these bottlenecks, a mosquito image database with fresh wild-caught specimens using basic smartphones is introduced, and we present a novel CNN-based architecture, VectorBrain, designed for identifying the species, sex, and abdomen status of a mosquito concurrently while being efficient and lightweight in computation and size. Overall, our proposed approach achieves 94.44±2% accuracy with a macro-averaged F1 score of 94.10±2% for the species classification, 97.66±1% accuracy with a macro-averaged F1 score of 96.17±1% for the sex classification, and 82.20±3.1% accuracy with a macro-averaged F1 score of 81.17±3% for the abdominal status classification. VectorBrain running on local mobile devices, paired with a low-cost handheld imaging tool, is promising in transforming the mosquito vector surveillance programs by reducing the burden of expertise required and facilitating timely response based on accurate monitoring.

Detection and classification on MRI images of brain tumor using YOLO NAS deep learning model

If a brain tumor is not properly diagnosed, it might result in fatal consequences and major health issues. As a result, a key component of diagnosis is the early identification of brain tumors and the precise categorization of brain tumor types. This research work focusses on detection and classification such as pituitary, meningioma, glioma, and non-tumorous tumors from brain tumor MRI image using YOLO NAS model. Advances in deep learning have led to an increasing awareness of computer-aided diagnosis technology. To improve classification performance, the input data set is acquired from the REMBRANDT repository using the Digital Database of Brain Tumor Magnetic Resonance Images. The primary phases of this task include segmentation, classification, and pre-processing. The researcher preprocessed the RGB image to exclude any undesired spots before locating the ROI. For each brain tumor image, we used the hybrid anisotropic diffusion filtering (HADF) technique to remove noise. The Encoder-Decoder Network (En–DeNet), which uses a deep neural network based on U-Net as the encoder and a pre-trained EfficientNet as the decoder, is then used to segment the MRI images. A proposed model was developed using a deep learning-based YOLO NAS technique to identify brain cancers from MRI images such as meningioma tumors, non-tumor, glioma tumors, and pituitary tumors. The suggested model's classification performance is weighed based on parameters such as precision, F1-score, sensitivity, accuracy, and specificity. The proposed fusion method YOLO NAS has improved classification results of accuracy (AC = 0.997%), specificity (SP = 0.985%), precision (PR = 0.982%), F1-score (F1 = 0.992%), and sensitivity (SE = 0.985%) using 2570 MRI data for training and 630 data for testing and validation of brain tumor cases. The results show that the brain tumor type classification system based on the proposed YOLO NAS technique works better than the DNN, PDCNN, DenseNet-161, and DCNN-SGD model classifiers.

CAM: a novel aid system to analyse the coloration quality of thick blood smears using image processing and machine learning techniques.

Battling malaria's morbidity and mortality rates demands innovative methods related to malaria diagnosis. Thick blood smears (TBS) are the gold standard for diagnosing malaria, but their coloration quality is dependent on supplies and adherence to standard protocols. Machine learning has been proposed to automate diagnosis, but the impact of smear coloration on parasite detection has not yet been fully explored. To develop Coloration Analysis in Malaria (CAM), an image database containing 600 images was created. The database was randomly divided into training (70%), validation (15%), and test (15%) sets. Nineteen feature vectors were studied based on variances, correlation coefficients, and histograms (specific variables from histograms, full histograms, and principal components from the histograms). The Machine Learning Matlab Toolbox was used to select the best candidate feature vectors and machine learning classifiers. The candidate classifiers were then tuned for validation and tested to ultimately select the best one. This work introduces CAM, a machine learning system designed for automatic TBS image quality analysis. The results demonstrated that the cubic SVM classifier outperformed others in classifying coloration quality in TBS, achieving a true negative rate of 95% and a true positive rate of 97%. An image-based approach was developed to automatically evaluate the coloration quality of TBS. This finding highlights the potential of image-based analysis to assess TBS coloration quality. CAM is intended to function as a supportive tool for analyzing the coloration quality of thick blood smears.

Individual Buffalo Identification Through Muzzle Dermatoglyphics Images using Deep Learning Approaches

Animal identification is essential for routine farm operations, residue traceback, insurance, and ownership management. Owing to their uniqueness, incorrigible nature, tamperproof over time, environment-friendly, and pain-free, visual biometrics-based animal identification has recently gained momentum over traditional animal identification methods. Among visual biometrics-based cues, muzzle identification is a simple and relatively low-cost method. Therefore, to address the inherent significant limitations of conventional animal identification systems, we undertook this investigation to collect a database of digital images of muzzles that works as a benchmark, to apply deep learning frameworks to identify individual buffaloes from their muzzle images, and to compare their accuracy in terms of their identification capabilities. Muzzle images of 198 Surti buffaloes were subjected to transfer learning and fine-tuning processes in deep-learning neural networks. The performance was recorded for each pre-train model (ResNet50, InceptionV3, VGG16, AlexNet) with different hyperparameters of the epoch, batch size, and learning rate. A perusal of the data revealed that ResNet50 has the highest train accuracy (99.8%) and test accuracy (99.69%) among all four models used. AlexNet has the lowest train accuracy (90.8%) among the models. The findings concluded that all these four models could be applied to identify individual buffaloes; however, ResNet50 had the highest accuracy, and deep learning applications have great potential for individual buffalo identification and are promising tools for precision livestock farming

Ensemble approach of deep learning models for binary and multiclass classification of histopathological images for breast cancer

Breast cancer (BC) is the most frequently occurring cancer disease observed in women after lung cancer. Out of different stages, invasive ductal BC causes maximum deaths in women. In this work, three deep learning (DL) models such as Vision Transformer (ViT), Convmixer, and Visual Geometry Group-19 (VGG-19) are implemented for the detection and classification of different breast cancer tumors with the help of Breast cancer histopathological (Break His) image database. The performance of each model is evaluated using an 80:20 training scheme and measured in terms of accuracy, precision, recall, loss, F1-score, and area under the curve (AUC). From the simulation result, ViT showed the best performance for binary classification of breast cancer tumors with accuracy, precision, recall, and F1-score of 99.89 %, 98.29 %, 98.29 %, and 98.29 %, respectively. Also, ViT showed the best performance in terms of accuracy (98.21 %), average Precision (89.84 %), recall (89.97 %), and F1-score (88.75) for eight class classifications. Moreover, we have also ensemble the ViT-Convmixer model and observed that the performance of the ensemble model is reduced as compared to the ViT model. We have also compared the performance of the proposed best model with other existing models reported by several research groups. The study will help find suitable models that will increase accuracy in early diagnoses of BC. We hope the study will also help to minimize human errors in the early diagnosis of this fatal disease and administer appropriate treatment. The proposed model may also be implemented for the detection of other diseases with improved accuracy.

Morphology of the normative human cone photoreceptor mosaic and a publicly available adaptive optics montage repository.

Adaptive optics ophthalmoscopy has enabled visualization of the in vivo human photoreceptor mosaic in health, disease and its treatment. Despite this, the clinical utility of the imaging technology has been limited by a lack of automated analysis techniques capable of accurately quantifying photoreceptor structure and a lack of an available normative image database. Here, we present a fully automated algorithm for estimating cone spacing and density over a complete adaptive optics montage along with a database of normative images and cone densities. We imaged the cone mosaics surrounding the fovea and along the horizontal and vertical meridians of fifty normal-sighted controls with a custom-built, multimodal adaptive optics scanning light ophthalmoscope. Cone spacing was automatically measured in the frequency domain and spacing measurements were converted to estimates of cone density at all locations across the montage. Consistent with previous reports, cone density measurements were highest near fovea (152,906 ± 53,209 cones/mm2) and decreased exponentially with eccentricity. A 2.5-fold variation was found in cone density estimates at 0.1mm, this variation decreased to 1.75-fold at 1mm. We provide all images, mosaic quantifications, and automated software open source. This database will aid investigators in translating adaptive optics ophthalmoscopy to clinical applications.

Ultrasonic characterization of defects in polycrystalline materials based on TFM image reconstruction using denoising diffusion probabilistic models

This paper presents a refined ultrasonic total focusing method (TFM) for accurately characterizing key defect parameters including size, orientation, and location in complex materials. The proposed approach aims to accurately reconstruct the defect shape in images compromised by grain interference. First, a TFM image database was generated with varying defect parameters and grain noise levels by adopting a custom forward modeling process based on superposition of defect and grain scattering data. Second, a new architecture called TFM-DDPM was then proposed which incorporates TFM and the conditional denoising diffusion probabilistic model (DDPM). Third, pixel-level uncertainty can be quantified through repeated sampling of noise which follows a standard normal distribution. An acceptance criterion for the reconstruction was further established by analyzing the quantified uncertainty. In simulation, the median Dice coefficient between the de-noised TFMs of 30° cracks and their corresponding ground truth images increased from 0.5585 to 0.9137 using the proposed approach, and the median IoU metric rose from 0.3875 to 0.8408. By applying the 6-dB drop approach to the reconstructed images, the root mean squared errors (RMSEs) of size, angle and location were decreased by 93.59%, 86.90% and 86.25%, respectively. It is also demonstrated that our method can reject 69.19% of incorrect characterization results caused by high levels of noise and/or images with steeply inclined (e.g., 45°) cracks, while accepting 97.29% accurate results. Experimental results obtained on nickel-based alloy K403 specimens also exhibited significant improvements. For 2–3 mm cracks, the average sizing error reduced from 0.46 mm to 0.13 mm, and the error in crack angle decreased from 28.63° to 1.50°. Comparable performance enhancements were observed for 4–5 mm cracks using the proposed approach.

Age estimation at death in individuals over 50 using CT images: An innovative anthropological approach

ObjectivesThe aim of the present work is to develop a novel method for predicting age in individuals over 50 years old, utilizing regression models. MethodsThe conducted study is of an analytical cross-sectional nature, involving a sample of 44 young subjects and 107 elderly subjects. The necessary data for this research were extracted from "The New Mexico Decedent Image Database." Based on the phenomenon of height shrinkage with age, we created models for young subjects and applied them to elderly subjects, allowing us to extract the variables. ResultsWe obtained highly encouraging results with an R2 of 0.73, a mean absolute error of 3.94, and stable cross-validation. We used a Student's t-test, which demonstrated no significant difference between predicted and actual values (p-value >0.05). We also conducted a learning curve analysis and examined residuals against predicted values. This suggests that the forecasts are accurate, with no significant bias in predictions. ConclusionThis work has allowed us to conclude that it is possible to reliably estimate the age of subjects over 50, taking into account age-related physiological and pathological changes.

A robust coverless image-synthesized video steganography based on asymmetric structure

Due to the ability of hiding secret information without modifying image content, coverless image stegonagraphy has gained higher level of security and become a research hot spot. However, in existing methods, the issue of image order disruption during network transmission is overlooked. In this paper, the image-synthesized video carrier is proposed for the first time. The selected images which represent secret information are synthesized to a video in order, thus the image order will not be disrupted during transmission and the effective capacity is greatly increased. Additionally, an asymmetric structure is designed to improve the robustness, in which only the receiver utilizes a robust image retrieval algorithm to restore secret information. Specifically, certain images are randomly selected from a public image database to create multiple coverless image datasets (MCIDs), with each image in a CID mapped to hash sequence. Images are indexed based on secret segments and synthesized into videos. After that, the synthesized videos are sent to the receiver. The receiver decodes the video into frames, identifies the corresponding CID of each frame, retrieves original image, and restores the secret information with the same mapping rule. Experimental results indicate that the proposed method outperforms existing methods in terms of capacity, robustness, and security.

Subjective and objective quality evaluation for industrial images

Recently, the demand for ever-better image processing technique continues to grow in field of industrial scenario monitoring and industrial process inspection. The subjective and objective quality evaluation of industrial images are vital for advancing the development of industrial visual perception and enhancing the quality of industrial image/video processing applications. However, the scarcity of publicly available industrial image databases with reliable subjective scores restricts the development of industrial image quality evaluation (IIQE). In preparation for a vacancy, this article first establishes two industrial image databases (i.e., industrial scenario image dataset (ISID) and industrial process image dataset (IPID)) for assessing IIQE metrics. Furthermore, in order to avoid overwhelming industrial image nuances due to the wavelet subband summation, we then present a novel industrial application subband information fidelity standard (SIFS) evaluation method using the channel capacity of visual signals in wavelet domain. Specifically, we first build a visual signals channel model based on perception process from human eyes to brain. Second, we compute and compare the channel capacity for reference and distorted images to measure the information fidelity in each wavelet subband. Third, we sum over the subbands for information fidelity ratio to obtain the overall quality score. Finally, we fairly compare some up-to-date and our proposed image quality evaluation (IQE) methods in two novelty industrial datasets respectively. Our ISID and IPID datasets are capable of evaluating most IQE metrics comprehensively and paves the way for further research on IIQE. Our SIFS model show a remarkable performance comparing with other up-to-date IQE methods.

Longitudinal Assessment of Retinopathy of Prematurity (LONGROP) Study: Impacts of Viewing Time and Ability to Compare on Detection of Change

This study compared two imaging grading techiques to assess the utility of longitudinal image-based analysis in retinopathy of prematurity (ROP) screening: 1) time-limited without image comparison (a proxy for bedside indirect ophthalmoscopy, termed sBIO) and time-unlimited with image comparison (for telemedicine grading, termed TELE) screening. We tested two hypotheses: 1) H1: TELE was superior to sBIO for the detection of change (Tempo)-same, better, or worse-and, 2) H2: granular data of change (e.g. at the image and feature level) is integrated by graders to achieve the Tempo assessment. Prospective reliability analysis. Gold standard reference (GS) was a published curated ROP image database consisting of both Tempo and granular level changes (image and components) from 40 patients in 2 sets. Graders were divided into 2 cohorts. There were two screening techniques-1) sBIO with time limited review of 10 minutes/patient, access to prior notes and drawings and 2) TELE with unlimited review time, access to prior weeks' images, notes and schematics. Graders switched techniques and sets after 6 weeks. H1 outcome was comparison of graders' weekly Tempo scores to GS-Gestalt and for H2 was Tempo score compared to GS-View and GS-Component. H1 demonstrated no difference-accuracy of sBIO and TELE compared to GS was 51.7% and 51.9% respectively (p=0.95). Highest agreement occurred when all exams exhibited no change (91.5% sBIO vs. 93.5% TELE, p=0.46) and worst agreement was when exams always demonstrated worsening (46.5% sBIO vs. 47.1% TELE, p=0.93). Both sets of graders did worse in weeks 7-12, irrespective of technique. H2 demonstrated that Tempo assessment did not correlate with granular data changes in the GS for View level and Component level assessments-overall agreement dropped to 31.4% for Tempo vs GS-VIEW (31.2% for sBIO, 31.5% for TELE) and 4.6% for Tempo vs GS-COMPONENT (4.9% for sBIO, 4.3% for TELE). Detection of ROP Tempo was independent of screening technique by expert pediatric retina graders. Both groups did significantly better in the first half of the study, indicative of a fatigue factor. This is the first study in ROP history to demonstrate that graders integrate image and retinal features in various ways that can be in contradiction of their assessment of overall disease progression.