Aims/Purpose: Autoimmune diseases are characterized by a pro‐inflammatory environment, with over‐release of chemokines, as IL‐6, IL‐1β and TNFα. These might have a pro‐vasodilation effect at a conjunctival level, with the clinical result of hyperaemia. Hyperaemia is hardly evaluable from a research point of view. The aim of this study is to investigate the use of conjunctival vessel density (CVD) as a marker of vessel dilation and, indirectly, of ocular surface inflammation, in a cohort of patients affected by autoimmune diseases. Methods: Study design: cross‐sectional, observational. CVD score was obtained analysing vessel‐enhanced images obtained by R‐scan module of Keratograph 5M (OCULUS Optikgeräte GmbH, Wetzlar, Germany). Images were loaded in ImageJ software package and in‐house scripts were used to analyse CVD at baseline and at follow‐up visit. CVD was obtained through the same methodology widely used in retinal research to study macular vessel density. Clinical data were collected and the correlations were reported. Results: We collected data from a cohort of patients suffering from autoimmune diseases. All the eyes were categorized considering both systemic and topic treatments, co‐existence of more autoimmune disorders, clinical signs and severity of dry eye. CVD values resulted significantly correlated with dry eye severity, in particular the higher was dry eye severity score, the higher was CVD ( p < 0.05). Conclusions: CVD is a new quantitative metric to objectively measure conjunctival hyperaemia; this diagnostic methodology resulted highly feasible to be performed in clinical practice, without inducing patients' distress, and resulted a parameter highly correlated with the pro‐inflammatory status of the patients. Reference 1.Singh RB, Liu L, Anchouche S, Yung A, Mittal SK, Blanco T et al. Ocular redness ‐ I: Aetiology, pathogenesis, and assessment of conjunctival hyperemia. Ocul Surf 2021. 2. Wu S, Hong J, Tian L, Cui X, Sun X, Xu J. Assessment of Bulbar Redness with a Newly Developed Keratograph. Optom Vis Sci 2015. 3. Arrigo A, Aragona E, Saladino A, Amato A, Bandello F, Battaglia Parodi M. The impact of different thresholds on optical coherence tomography angiography images binarization and quantitative metrics. Sci Rep 2021.

Digital reconstructions of numerical holograms enable data visualization and serve a multitude of purposes ranging from microscopy to holographic displays. Over the years, many pipelines have been developed for specific hologram types. Within the standardization effort of JPEG Pleno holography, an open-source MATLAB toolbox was developed that reflects the best current consensus. It can process Fresnel, angular spectrum, and Fourier-Fresnel holograms with one or more color channels; it also allows for diffraction-limited numerical reconstructions. The latter provides a way to reconstruct holograms at their intrinsic physical instead of an arbitrarily chosen numerical resolution. The Numerical Reconstruction Software for Holograms v10 supports all large public data sets featured by UBI, BCOM, ETRI, and ETRO, in their native and vertical off-axis binary forms. Through the release of this software, we hope to improve the reproducibility of research, thus enabling consistent comparison of data between research groups and the quality of specific numerical reconstructions.

The aim of the study was to characterize macular edema (ME) in retinitis pigmentosa (RP) by means of quantitative optical coherence tomography (OCT)-based imaging. The study was designed as observational, prospective case series, with 1-year follow-up. All RP patients underwent complete ophthalmologic assessment, including structural OCT, OCT angiography, and microperimetry (MP). The primary outcome was the characterization through quantitative OCT-based imaging of RP eyes complicated by ME. A total of 68 RP patients’ eyes (68 patients) and 68 eyes of 68 healthy controls were recruited. Mean BCVA was 0.14 ± 0.17 LogMAR at baseline and 0.18 ± 0.23 LogMAR at 1-year follow-up (p > 0.05). Thirty-four eyes (17 patients; 25%) showed ME, with a mean ME duration of 8 ± 2 months. Most of the eyes were characterized by recurrent ME. The ME was mainly localized in the inner nuclear layer in all eyes. LogMAR BCVA was similar in all RP eyes, whether with or without ME, although those with ME were associated with higher vessel density values, as well as thicker choroidal layers, than those without ME. In conclusion, the inner retina is closely involved in the pathogenesis of ME. The impairment of retinal-choroidal exchanges and Müller cell disruption might be a major pathogenic factor leading to the onset of ME in RP.

While 60 years of successful application of holography is celebrated in this special issue, efficient representation and compression of holographic data has received relatively little attention in research. Notwithstanding this observation, and particularly due to the digitization that is also penetrating the holographic domain, interest is growing on how to efficiently compress holographic data such that interactive exchange of content, as well as digital storage can be facilitated proficiently. This is a particular challenge, not only because of its interferometric nature and the various representation formats, but also the often extremely large data volumes involved in pathological, tomographic, or high-end visualization applications. In this paper, we provide an overview of the state of the art in compression techniques and corresponding quality metrics for various practical applications in digital holography. We also consider the future by analyzing the emerging trends for addressing the key challenges in this domain.

Background To investigate the morphological retinal parameters associated with retinal sensitivity status in retinitis pigmentosa (RP) through a quantitative multimodal imaging approach. Methods The study was designed as an observational, prospective case series, including RP patients and healthy controls. Multimodal imaging included fundus autofluorescence (FAF), structural optical coherence tomography (OCT), OCT angiography (OCTA) and microperimetry (MP). The follow-up lasted 12 months. For each imaging modality, we performed an overall quantitative analysis and a detailed investigation based on the ETDRS-9 sectors grid. Quantitative parameters included the thickness of each retinal and choroidal layer, vessel density (VD), choriocapillaris porosity (CCP), FAF intensity and MP retinal sensitivity. Results We included 40 eyes (40 patients) affected by RP and 40 healthy eyes (40 controls). Mean baseline BCVA was 0.14 ± 0.18 LogMAR, with 0.18 ± 0.24 LogMAR after 1-year of follow-up. RP eyes showed statistically significant alterations of retinal and choroidal layers on the ETDRS-9 sectors grid, significant reduction of VD values and MP retinal sensitivity, and significantly higher CCP than controls. The inner retinal layers proved closely associated with the functional integrity of the posterior pole. In addition, our ROC analysis provided quantitative cutoffs connected significantly with a high probability of observing a partial sparing of MP retinal sensitivity. Conclusions The inner retinal layers are closely associated with the functional integrity of the posterior pole in RP. FAF intensity reduction may be interpreted as lipofuscin metabolism impairment inducing increased phototoxic distress for retinal structures. Vascular involvement contributes to the morpho-functional deterioration of the macular region in RP.

There exist a multitude of methods and processing steps for the numerical reconstruction of digital holograms. Because these are not standardized, most research groups follow their own best practices, making it challenging to compare numerical results across groups. Meanwhile, JPEG Pleno holography seeks to define a new standard for the compression of digital holograms. Numerical reconstructions are an essential tool in this research because they provide access to the holographically encoded 3D scenes. In this paper, we outline the available modules of the numerical reconstruction software developed for the purpose of this standardization. A software package was defined that is able to reconstruct all holograms of the JPEG Pleno digital hologram database, evaluating several core experiments. This includes Fresnel holograms recorded or generated in Fresnel or (lensless) Fourier geometries as well as near-field holograms, that require rigorous propagation through the angular spectrum method. Specific design choices are explained and highlighted. We believe that providing information on the current consensus on the numerical reconstruction software package will allow other research groups to replicate the results of JPEG Pleno and improve comparability of results.

This paper reports the status of the JPEG Pleno standard for the light field imaging modality and discusses current activities towards future developments of the standard. Currently, three parts (Part 1: Framework, Part 2: Light field coding with Amendment 1: Profiles and levels for JPEG Pleno light field coding system and Part 3: Conformance testing) have been standardized. Part 4: Reference software is in the final approval stage before standardization. The four-dimensional nature of plenoptic data poses many challenges. The literature presents several subjective and objective assessment models for the evaluation of two-dimensional (image/video) data. As for plenoptic data this topic is still under investigation, one of the JPEG Pleno standardization activities targets the development of plenoptic image quality assessment standard. Finally, as one of the possible directions within future JPEG Pleno standardization activities, JPEG Pleno is currently exploring state-of-the-art light field coding architectures exploiting learning-based approaches to assess the potential of these coding methods in terms of compression efficiency.

The rise of interest in Building Information Modelling (BIM) during the last years has led to the introduction of complex data structures. Among all this information, a precise definition of structures in the form of point clouds is still an open research area. Consequently, an accurate, fast and easy to use point cloud acquisition system is needed. In this paper, we present a hardware-software prototype for automated indoor environments scanning through the acquisition of point clouds using multiple low-cost depth sensors. The acquisition and processing phase are described in detail, as well as the test environments used to assess the system functionality.

Optical coherence tomography (OCT) angiography (OCTA) is a non-invasive tool for the in-vivo study of the intraretinal vascular network. It is based on the analysis of motion particles within the retina to reconstruct the paths followed by the erythrocytes, i.e. retinal capillaries. To date, qualitative and quantitative information are based on the morphological features disclosed by retinal capillaries. In the present study, we proposed new quantitative functional metrics, named Total Flow Intensity (TFI), Active Flow Intensity (AFI), and Volume-related Flow Intensity (VFI), based on the processing of the blood flow signal detected by OCTA. We studied these metrics in a cohort of healthy subjects, and we assessed their clinical utility by including a cohort of age-matched patients affected by Stargardt disease. Moreover, we compared TFI, AFI, and VFI to the widely used vessel density (VD) parameter. TFI, AFI, and VFI were able to describe in detail the different properties of the retinal vascular compartment. In particular, TFI was intended as the overall amount of volumetric retinal blood flow. AFI represented a selective measure of voxels disclosing blood flow signal. VFI was developed to put in relationship the volumetric blood flow information with the not vascularized retinal volume. In conclusion, TFI, AFI, and VFI were proposed as feasible functional OCTA biomarkers based on the analysis of retinal blood flow signal.

Sea ice has a significant effect on climate change and ship navigation. Hence, it is crucial to draw sea-ice maps that reflect the geographical distribution of different types of sea ice. Many automatic sea-ice classification methods using synthetic aperture radar (SAR) images are based on the polarimetric characteristics or image texture features of sea ice. They either require professional knowledge to design the parameters and features or are sensitive to noise and condition changes. Moreover, ice changes over time are often ignored. In this article, we propose a new SAR sea-ice image classification method based on a combined learning of spatial and temporal features, derived from residual convolutional neural networks (ResNet) and long short-term memory (LSTM) networks. In this way, we achieve automatic and refined classification of sea-ice types. First, we construct a seven-type ice data set according to the Canadian Ice Service ice charts. We extract spatial feature vectors of a time series of sea-ice samples using a trained ResNet network. Then, using the feature vectors as inputs, the LSTM network further learns the variation of the set of sea-ice samples with time. Finally, the extracted high-level features are fed into a softmax classifier to output the most recent ice type. Taking both spatial features and time variation into consideration, our method can achieve a high classification accuracy of 95.7% for seven ice types. Our method can automatically produce more objective sea-ice interpretation maps, allowing detailed sea-ice distribution and improving the efficiency of sea-ice monitoring tasks.

Digital holography is an imaging technique with great potential, that allows the acquisition and the reproduction of the entire wavefield of light. A big challenge in this field is the development of efficient coding tools, due to the different hologram data characteristics compared to standard multimedia content. This work presents an experimental comparison between two different coding techniques: the well-known hologram plane coding and the less-investigated object plane coding. In order to identify advantages and limitations of each method, but also to determine if the object plane coding could be an effective alternative to the more simple hologram plane coding, different types of holograms are employed, analyzing and discussing the obtained results.

The low accessibility to the information regarding buildings current performances causes deep difficulties in planning appropriate interventions. Internet of Things (IoT) sensors make available a high quantity of data on energy consumptions and indoor conditions of an existing building that can drive the choice of energy retrofit interventions. Moreover, the current developments in the topic of the digital twin are leading the diffusion of Building Information Modeling (BIM) methods and tools that can provide valid support to manage all data and information for the retrofit process. This paper shows the aim and the findings of research focused on testing the integrated use of BIM methodology and IoT systems. A common data platform for the visualization of building indoor conditions (e.g., temperature, luminance etc.) and of energy consumption parameters was carried out. This platform, tested on a case study located in Italy, is developed with the integration of low-cost IoT sensors and the Revit model. To obtain a dynamic and automated exchange of data between the sensors and the BIM model, the Revit software was integrated with the Dynamo visual programming platform and with a specific Application Programming Interface (API). It is an easy and straightforward tool that can provide building managers with real-time data and information about the energy consumption and the indoor conditions of buildings, but also allows for viewing of the historical sensor data table and creating graphical historical sensor data. Furthermore, the BIM model allows the management of other useful information about the building, such as dimensional data, functions, characteristics of the components of the building, maintenance status etc., which are essential for a much more conscious, effective and accurate management of the building and for defining the most suitable retrofit scenarios.

Recently, the ISO/IEC JTC 1/SC 29/WG 1 standardization committee has been developing a new image compression standard known as JPEG Pleno. This standard aims to facilitate the capture, representation, and exchange of plenoptic imaging modalities such as light fields, point clouds, and holography. This paper presents a brief overview of the current status of JPEG Pleno series of standards and coding architecture as defined in Part 1 and Part 2 of the standard, and a detailed discussion of the current activities on defining the conformance tests and the reference software parts within the standard.

We propose a device for monitoring the number of people who are physically present inside indoor environments. The device performs local processing of infrared array sensor data detecting people’s direction, which allows monitoring users’ occupancy in any space of the building and also respects people privacy. The device implements a novel real-time pattern recognition algorithm for processing data sensed by a low-cost infrared (IR) array sensor. The computed information is transferred through a Z-Wave network. On-field evaluation of the algorithm has been conducted by placing the device on top of doorways in offices and laboratory rooms. To evaluate the performance of the algorithm in varying ambient temperatures, two groups of stress tests have been designed and performed. These tests established the detection limits linked to the difference between the average ambient temperature and perturbation. For an in-depth analysis of the accuracy of the algorithm, synthetic data have been generated considering temperature ranges typical of a residential environment, different human walking speeds (normal, brisk, running), and distance between the person and the sensor (1.5 m, 5 m, 7.5 m). The algorithm performed with high accuracy for routine human passage detection through a doorway, considering indoor ambient conditions of 21–30 °C.

Müller cells (MC) represent a key element for the metabolic and functional regulation of the vertebrate retina. The aim of the present study was to test the feasibility of a new method for the in-vivo detection and quantification of extrafoveal MC in human retina. We developed a new approach to isolate and analyse extrafoveal MC in vivo, starting from structural optical coherence tomography data. Our pilot investigation was based on the optical properties of MC, which are known to not interfere with the light reaching the outer retinal structures. We reconstructed MC in the macular region of 18 healthy subjects and the quantitative analyses revealed ~42,000/9 mm2 cells detected. Furthermore, we included 2 patients affected by peripheral intraocular melanoma, with macular sparing, needing surgical enucleation. We used these two eyes to perform a qualitative comparison between our reconstructions and histological findings. Our study represents the first pilot investigation dedicated on the non-invasive isolation and quantification of MC, in-vivo, in human retina. Although we are aware that our study has several limitations, first of all related with the proper detection of foveal MC, because of the peculiar z-shape morphology, this approach may open new opportunities for the non-invasive in vivo analysis of MC, providing also potential useful perspectives in retinal diseases.

JPEG Pleno is a standardization framework addressing the compression and signaling of plenoptic modalities. While the standardization of solutions to handle light field content is currently reaching its final stage, the Joint Photographic Experts Group (JPEG) committee is now preparing for the standardization of solutions targeting point cloud and holographic modalities. This paper addresses the challenges related to the standardization of compression technologies for holographic content and associated test methodologies.

JPEG Pleno is a standardization framework addressing the compression and signalling of plenoptic modalities. While the standardization of solutions to handle light field content is currently reaching its final stage, the JPEG committee is now preparing for the standardization of solutions targeting point cloud and holographic modalities. This paper addresses the challenges related to the standardization of compression technologies for holographic content and associated test methodologies.

Digital holography needs efficient coding tools that facilitate storage and transmission of this type of data in order to reach practical applications. This paper presents an experimental analysis of the performance of different coding tools for the compression of digital holograms. During the experiments, a dedicated compression architecture is employed in order to transform the holographic data in a representation suitable to be provided to the encoders, and for performing an objective quality evaluation of the obtained results. Several state-of-the-art image and video codecs are evaluated on different reference datasets, comprising different types of digital holograms. The evaluation is carried out on the reconstructed images with different metrics, and obtained results are critically analyzed and discussed.

The Sea Surface Temperature (SST) is one of the key factors affecting ocean climate change. Hence, Sea Surface Temperature Prediction (SSTP) is of great significance to the study of navigation and meteorology. However, SST data is well-known to suffer from high levels of redundant information, which makes it very difficult to realize accurate predictions, for instance when using time-series regression. This paper constructs a simple yet effective SSTP model, dubbed DSL (given its origination from methods known as DTW, SVM and LSPSO). DSL is based on time-series similarity measure, multiple pattern learning and parameter optimization. It consists of three parts: (1) using Dynamic Time Warping (DTW) to mine the similarities in historical SST series; (2) training a Support Vector Machine (SVM) using the top-k similar patterns, deriving a robust SSTP model that offers a 5-day prediction window based on multiple SST input sequences; and (3) developing an improved Particle Swarm Optimization (PSO) method, dubbed LSPSO, which uses a local search strategy to achieve the combined requirement of prediction accuracy and efficiency. Our method strives for optimal model parameters (pattern length and interval step) and is suited for long-term series, leading to significant improvements in SST trend predictions. Our experimental validation shows a 16.7% reduction in prediction error, at a 76% gain in operating efficiency. We also achieve a significant improvement in prediction accuracy of non-stationary SST time series, compared to DTW, SVM, DS (i.e., DTW + SVM), and a recent deep learning method dubbed Long-Short Term Memory (LSTM).