Colored Texture Analysis Fuzzy Entropy Methods with a Dermoscopic Application.

Texture analysis is a subject of intensive focus in research due to its significant role in the field of image processing. However, few studies focus on colored texture analysis and even fewer use information theory concepts. Entropy measures have been proven competent for gray scale images. However, to the best of our knowledge, there are no well-established entropy methods that deal with colored images yet. Therefore, we propose the recent colored bidimensional fuzzy entropy measure, FuzEnC2D, and introduce its new multi-channel approaches, FuzEnV2D and FuzEnM2D, for the analysis of colored images. We investigate their sensitivity to parameters and ability to identify images with different irregularity degrees, and therefore different textures. Moreover, we study their behavior with colored Brodatz images in different color spaces. After verifying the results with test images, we employ the three methods for analyzing dermoscopic images of malignant melanoma and benign melanocytic nevi. FuzEnC2D, FuzEnV2D, and FuzEnM2D illustrate a good differentiation ability between the two-similar in appearance-pigmented skin lesions. The results outperform those of a well-known texture analysis measure. Our work provides the first entropy measure studying colored images using both single and multi-channel approaches.

Image quality of compressive single-pixel imaging using different Hadamard orderings.

Single-pixel imaging is an imaging technique that has recently attracted a lot of attention from several areas. This paper presents a study on the influence of the Hadamard basis ordering on the image reconstruction quality, using simulation and experimental methods. During this work, five different orderings, Natural, Walsh, Cake-cutting, High Frequency and Random orders, along with two different reconstruction algorithms, TVAL3 and NESTA, were tested. Also, three different noise levels and compression ratios from 0.1 to 1 were evaluated. A single-pixel camera was developed using a digital micromirror device for the experimental phase. For a compression ratio of 0.1, the Cake-cutting order achieved the best reconstruction quality, while the best contrast was achieved by Walsh order. For compression ratios of 0.5, the Walsh and Cake-cutting orders achieved similar results. Both Walsh and Cake-cutting orders reconstructed the images with good quality using compression ratios from 0.3. Finally, the TVAL3 algorithm showed better image reconstruction quality, in comparison with NESTA, when considering compression ratios from 0.1 to 0.5.

Impact of culvert flooding on carnivore crossings.

Along many roads worldwide, drainage culverts are the only structures wildlife can safely use to cross. However, culverts inundate and can become unavailable to terrestrial fauna during rainy periods. We conducted a field study over wet and dry seasons in southern Portugal to assess the effect of culvert flooding on crossings by medium-sized carnivores. We set up track stations inside 30 culverts along intermediate-level traffic roads to evaluate complete crossings (n = 1211) and used mixed-effects models to quantify the effects. Carnivores were more likely to cross and crossed more frequently if the culvert had a natural dry pathway at the time of the crossing. Carnivores were also more likely to cross culverts with streams running through them. Moreover, culverts with flowing streams during the wet season were still more likely to be crossed during the dry season when the streams were dry. The significance of the difference in crossing rates between wet and dry seasons was species-specific. Our study reveals that flowing water and dry pathways jointly contribute to promoting crossings by this carnivore community. Culverts including streams may act as a continuation of riparian corridors, being incorporated into carnivores' movement routes. Our results lend empirical support to recommendations advising the implementation of dry pathways to provide crossing paths. Interventions to offset the transient impacts of water flooding in new or existing culverts can be a cost-effective solution promoting connectivity across roads allowing movement of individuals.

Unravelling associations between tree-seedling performance, herbivory, competition, and facilitation in high nature value farmlands.

Herbivory, plant facilitation, and competition have complex impacts on tree regeneration which are seldom investigated together. Grazing exclosure experiments have allowed quantification of the effects of large herbivores on tree regeneration dynamics but have often ignored the effect of herbivorous insects. We experimentally tested how folivory (percentage of leaf damaged by insects) and microenvironment (tree canopy cover and herbs) jointly alter performance (growth and survival) of seedlings of two dominant Mediterranean oak-species within ungulate exclosures in a 3-year field study. An agroforestry system dominated by cork oak (Quercus suber) and holm oak (Q. rotundifolia) was assessed in south-east Portugal. We aimed also to determine whether the two oak species differed in the interdependences between folivory, microenvironment, covaring factors, and seedling performance. Unexpectedly, under the low-moderate insect defoliation, growth and survival of cork and holm oak seedlings were positively associated with herbivore damage. Herb removal increased oak folivory by 1.4 times. Herb removal was also positively associated with growth, directly and indirectly through its negative effect on oak folivory. Tree canopy favored insect folivory upon cork oak seedlings directly and upon holm oak indirectly via decreasing light availability. Folivory was threefold greater upon cork than upon holm oak-seedlings. Our study shows that tree canopy, herbs, and covarying factors can affect cork and holm oak-seedling performances through complex pathways, which markedly differ for the two species. The combined effect of insect herbivory and positive and negative plant-plant interactions need to be integrated into future tree regeneration efforts toward tackling the regeneration crisis of oak agroforestry systems of the Mediterranean.

Bidimensional ensemble entropy: Concepts and application to emphysema lung computerized tomography scans.

BACKGROUND AND OBJECTIVE: Bidimensional entropy algorithms provide meaningful quantitative information on image textures. These algorithms have the advantage of relying on well-known one-dimensional entropy measures dedicated to the analysis of time series. However, uni- and bidimensional algorithms require the adjustment of some parameters that influence the obtained results or even findings. To address this, ensemble entropy techniques have recently emerged as a solution for signal analysis, offering greater stability and reduced bias in data patterns during entropy estimation. However, such algorithms have not yet been extended to their two-dimensional forms. METHODS: We therefore propose six bidimensional algorithms, namely ensemble sample entropy, ensemble permutation entropy, ensemble dispersion entropy, ensemble distribution entropy, and two versions of ensemble fuzzy entropy based on different models or parameters initialization of an entropy algorithm. These new measures are first tested on synthetic images and further applied to a biomedical dataset. RESULTS: The results suggest that ensemble techniques are able to detect different levels of image dynamics and their degrees of randomness. These methods lead to more stable entropy values (lower coefficients of variations) for the synthetic data. The results also show that these new measures can obtain up to 92.7% accuracy and 88.4% sensitivity when classifying patients with pulmonary emphysema through a k-nearest neighbors algorithm. CONCLUSIONS: This is a further step towards the potential clinical deployment of bidimensional ensemble approaches to detect different levels of image dynamics and their successful performance on emphysema lung computerized tomography scans. These bidimensional ensemble entropy algorithms have potential to be used in various imaging applications thanks to their ability to distinguish more stable and less biased image patterns compared to their original counterparts.

Retinal OCT speckle as a biomarker for glaucoma diagnosis and staging.

This paper presents a novel image analysis strategy that increases the potential of macular Optical Coherence Tomography (OCT) by using speckle features as biomarkers in different stages of glaucoma. A large pool of features (480) were computed for a subset of macular OCT volumes of the Leuven eye study cohort. The dataset contained 258 subjects that were divided into four groups based on their glaucoma severity: Healthy (56), Mild (94), Moderate (48), and Severe (60). The OCT speckle features were categorized as statistical properties, statistical distributions, contrast, spatial gray-level dependence matrices, and frequency domain features. The averaged thicknesses of ten retinal layers were also collected. Kruskal-Wallis H test and multivariable regression models were used to infer the most significant features related to glaucoma severity classification and to the correlation with visual field mean deviation. Four features were selected as being the most relevant: the ganglion cell layer (GCL) and the inner plexiform layer (IPL) thicknesses, and two OCT speckle features, the data skewness computed on the retinal nerve fiber layer (RNFL) and the scale parameter (a) of the generalized gamma distribution fitted to the GCL data. Based on a significance level of 0.05, the regression models revealed that RNFL skewness exhibited the highest significance among the features considered for glaucoma severity staging (p-values of 8.6×10-6 for the logistic model and 2.8×10-7 for the linear model). Furthermore, it demonstrated a strong negative correlation with the visual field mean deviation (ρ=-0.64). The post hoc analysis revealed that, when distinguishing healthy controls from glaucoma subjects, GCL thickness is the most relevant feature (p-value of 8.7×10-5). Conversely, when comparing the Mild versus Moderate stages of glaucoma, RNFL skewness emerged as the only feature exhibiting statistical significance (p-value = 0.001). This work shows that macular OCT speckle contains information that is currently not used in clinical practice, and not only complements structural measurements (thickness) but also has a potential for glaucoma staging.

Signal-carrying speckle in optical coherence tomography: a methodological review on biomedical applications.

SIGNIFICANCE: Speckle has historically been considered a source of noise in coherent light imaging. However, a number of works in optical coherence tomography (OCT) imaging have shown that speckle patterns may contain relevant information regarding subresolution and structural properties of the tissues from which it is originated. AIM: The objective of this work is to provide a comprehensive overview of the methods developed for retrieving speckle information in biomedical OCT applications. APPROACH: PubMed and Scopus databases were used to perform a systematic review on studies published until December 9, 2021. From 146 screened studies, 40 were eligible for this review. RESULTS: The studies were clustered according to the nature of their analysis, namely static or dynamic, and all features were described and analyzed. The results show that features retrieved from speckle can be used successfully in different applications, such as classification and segmentation. However, the results also show that speckle analysis is highly application-dependant, and the best approach varies between applications. CONCLUSIONS: Several of the reviewed analyses were only performed in a theoretical context or using phantoms, showing that signal-carrying speckle analysis in OCT imaging is still in its early stage, and further work is needed to validate its applicability and reproducibility in a clinical context.

Three-Dimensional Multiscale Fuzzy Entropy: Validation and Application to Idiopathic Pulmonary Fibrosis.

Idiopathic Pulmonary Fibrosis (IPF) is a chronic, severe, and progressive lung disease with short life expectancy. Based on information theory and entropy measurement, a three-dimensional multiscale fuzzy entropy (MFE 3D) algorithm is proposed to identify IPF patients from their computed tomography (CT) volumetric data. First, the validation of the algorithm was performed by analyzing several volumetric synthetic noises (white, blue, brown, and pink), MIX(p) processes-based volumes, and texture-based volumes. The entropy values obtained by MFE 3D were consistent with the values obtained using the one, and two-dimensional versions, validating its use in biomedical data. Hence, MFE 3D was applied to CT scans to identify the existence of IPF within two different groups, one of healthy subjects (26) and another of IPF patients (26). Statistical differences were found (p < 0.05) between the entropy values of each group in 5 scale factors out of 10. These results demonstrate that MFE 3D could be an interesting metric to identify IPF in CT scans.

Evaluation of COVID-19 chest computed tomography: A texture analysis based on three-dimensional entropy.

Radiologists, and doctors in general, need relevant information for the quantification and characterization of pulmonary structures damaged by severe diseases, such as the Coronavirus disease 2019 (COVID-19). Texture-based analysis in scope of other pulmonary diseases has been used to screen, monitor, and provide valuable information for several kinds of diagnoses. To differentiate COVID-19 patients from healthy subjects and patients with other pulmonary diseases is crucial. Our goal is to quantify lung modifications in two pulmonary pathologies: COVID-19 and idiopathic pulmonary fibrosis (IPF). For this purpose, we propose the use of a three-dimensional multiscale fuzzy entropy (MFE3D) algorithm. The three groups tested (COVID-19 patients, IPF, and healthy subjects) were found to be statistically different for 9 scale factors ( p < 0.01 ). A complexity index (CI) based on the sum of entropy values is used to classify healthy subjects and COVID-19 patients showing an accuracy of 89.6 % , a sensitivity of 96.1 % , and a specificity of 76.9 % . Moreover, 4 different machine-learning models were also used to classify the same COVID-19 dataset for comparison purposes.

Optical Coherence Tomography Imaging of the Lamina Cribrosa: Structural Biomarkers in Nonglaucomatous Diseases.

The lamina cribrosa (LC) is an active structure that responds to the strain by changing its morphology. Abnormal changes in LC morphology are usually associated with, and indicative of, certain pathologies such as glaucoma, intraocular hypertension, and myopia. Recent developments in optical coherence tomography (OCT) have enabled detailed in vivo studies about the architectural characteristics of the LC. Structural characteristics of the LC have been widely explored in glaucoma management. However, information about which LC biomarkers could be useful for the diagnosis, and follow-up, of other diseases besides glaucoma is scarce. Hence, this literature review aims to summarize the role of the LC in nonophthalmic and ophthalmic diseases other than glaucoma. PubMed was used to perform a systematic review on the LC features that can be extracted from OCT images. All imaging features are presented and discussed in terms of their importance and applicability in clinical practice. A total of 56 studies were included in this review. Overall, LC depth (LCD) and thickness (LCT) have been the most studied features, appearing in 75% and 45% of the included studies, respectively. These biomarkers were followed by the prelaminar tissue thickness (21%), LC curvature index (5.4%), LC global shape index (3.6%), LC defects (3.6%), and LC strains/deformations (1.8%). Overall, the disease groups showed a thinner LC (smaller LCT) and a deeper ONH cup (larger LCD), with some exceptions. A large variability between approaches used to compute LC biomarkers has been observed, highlighting the importance of having automated and standardized methodologies in LC analysis. Moreover, further studies are needed to identify the pathologies where LC features have a diagnostic and/or prognostic value.

Effect of static scatterers in laser speckle contrast imaging: an experimental study on correlation and contrast.

Laser speckle contrast imaging (LSCI) is a non-invasive microvascular blood flow assessment technique with good temporal and spatial resolution. Most LSCI systems, including commercial devices, can perform only qualitative blood flow evaluation, which is a major limitation of this technique. There are several factors that prevent the utilization of LSCI as a quantitative technique. Among these factors, we can highlight the effect of static scatterers. The goal of this work was to study the influence of differences in static and dynamic scatterer concentration on laser speckle correlation and contrast. In order to achieve this, a laser speckle prototype was developed and tested using an optical phantom with various concentrations of static and dynamic scatterers. It was found that the laser speckle correlation could be used to estimate the relative concentration of static/dynamic scatterers within a sample. Moreover, the speckle correlation proved to be independent of the dynamic scatterer velocity, which is a fundamental characteristic to be used in contrast correction.

Laser Speckle Imaging to Monitor Microvascular Blood Flow: A Review.

Laser speckle is a complex interference phenomenon that can easily be understood, in concept, but is difficult to predict mathematically, because it is a stochastic process. The use of laser speckle to produce images, which can carry many types of information, is called laser speckle imaging (LSI). The biomedical applications of LSI started in 1981 and, since then, many scientists have improved the laser speckle theory and developed different imaging techniques. During this process, some inconsistencies have been propagated up to now. These inconsistencies should be clarified in order to avoid errors in future works. This review presents a review of the laser speckle theory used in biomedical applications. Moreover, we also make a review of the practical concepts that are useful in the construction of laser speckle imagers. This study is not only an exposition of the concepts that can be found in the literature but also a critical analysis of the investigations presented so far. Concepts like scatterers velocity distribution, effect of static scatterers, optimal speckle size, light penetration angle, and contrast computation algorithms are discussed in detail.

Impact of nutrition and salinity changes on biological performances of green and white sturgeon.

Green and white sturgeon are species of high conservational and economic interest, particularly in the San Francisco Bay Delta (SFBD) for which significant climate change-derived alterations in salinity and nutritional patterns are forecasted. Although there is paucity of information, it is critical to test the network of biological responses underlying the capacity of animals to tolerate current environmental changes. Through nutrition and salinity challenges, climate change will likely have more physiological effect on young sturgeon stages, which in turn may affect growth performance. In this study, the two species were challenged in a multiple-factor experimental setting, first to levels of feeding rate, and then to salinity levels for different time periods. Data analysis included generalized additive models to select predictors of growth performance (measured by condition factor) among the environmental stressors considered and a suite of physiological variables. Using structural equation modeling, a path diagram is proposed to quantify the main linkages among nutrition status, salinity, osmoregulation variables, and growth performances. Three major trends were anticipated for the growth performance of green and white sturgeon in the juvenile stage in the SFBD: (i) a decrease in prey abundance will be highly detrimental for the growth of both species; (ii) an acute increase in salinity within the limits studied can be tolerated by both species but possibly the energy spent in osmoregulation may affect green sturgeon growth within the time window assessed; (iii) the mechanism of synergistic effects of nutrition and salinity changes will be more complex in green sturgeon, with condition factor responding nonlinearly to interactions of salinity and nutrition status or time of salinity exposure. Green sturgeon merits special scientific attention and conservation effort to offset the effects of feed restriction and salinity as key environmental stressors in the SFBD.