Image technique for wound assessment

Two photos of the famous Mona Lisa were taken in October 2004 to contribute towards improving the technical and scientific data of the masterpiece before moving it in April 2005. The capabilities of the multispectral system developed by the LUMIERE TECHNOLOGY Company within the scope of the European CRISATEL project have made it possible to achieve optimal spatial resolution on the masterpiece, the highest level of sharpness ever achieved in the field of infrared reflectography, and a level of accuracy regarding color information which cannot be achieved with traditional technologies. From ultraviolet to infrared, all the details enhance the intimacy of this masterpiece. The scientific utilization of this data will undoubtedly be a major breakthrough to achieve better knowledge of Leonard De Vinci's work. The remarkable nature of this masterpiece and its specific physical features have required appropriate parameterizations and preliminary tests which are described in this publication.

Ocean color (OC) monitoring using satellite imageries provides an appropriate tool for a better understanding of marine processes and changes in the coastal environment. Radiance measurements in the range of visible light of the electromagnetic spectrum provides information of ocean color that is associated with the water constituents. This measurements are used to monitor the level of biological activity and the presence of particles in the water. Ocean features such as the concentration of chlorophyll, suspended sediment concentration and sea surface temperature have a significant impact on the dynamics of the ocean. The concentration of chlorophyll (<i>chla</i>), active pigments of phytoplankton photosynthesis, as a key indicator applied for assessment of water quality and biochemistry. Experimental algorithms <i>chla</i> related to internal communication various optical components in the water that may be change in space and time in the water with different optical characteristics. Therefore, the algorithms have been developed for one area may not work for other places and each region according to its specific characteristics needs that determined by an algorithm may be appropriate to local. We have tried treatment several algorithms for determination of chlorophyll, including experimental algorithms with a simple band ratio of blue-green band (i.e. OCx) and algorithms includes two bands ratio with variable 𝑅<sub>𝑟𝑠</sub>(λ<sub>2</sub>)/𝑅<sub>𝑟𝑠</sub>(λ<sub>1</sub>), the three bands ratio with variable [𝑅<sub>𝑟𝑠</sub>(λ<sub>1</sub>)−1−𝑅<sub>𝑟𝑠</sub>(λ<sub>2</sub>)−1]×𝑅<sub>𝑟𝑠</sub>(λ<sub>3</sub>) and four bands ratio with variable [𝑅<sub>𝑟𝑠</sub>(λ<sub>1</sub>)−1−𝑅<sub>𝑟𝑠</sub>(λ<sub>2</sub>)−1]/[𝑅<sub>𝑟𝑠</sub>(λ<sub>4</sub>)−1−𝑅<sub>𝑟𝑠</sub>(λ<sub>3</sub>)−1] that desired wavelength (i.e. λ<sub>1</sub>, λ<sub>2</sub>, λ<sub>3</sub> and λ<sub>4</sub>) in the range of red and near-infrared wavelengths of the electromagnetic spectrum are in the region of the Persian Gulf and Oman Sea look. Despite the high importance of the Persian Gulf and Oman Sea which can have up basin countries, to now few studies have been done in this area. The focus of this article on the northern part of Oman Sea and Persian Gulf, the shores of neighboring Iran (case 2 water). In this paper, by using Landsat 8 satellite imageries, we have discussed chla concentrations and customizing different OC algorithms for this new dataset (Landsat-8 imagery). This satellite was launched in 2013 and its data using two sensors continuously are provided operating one sensor imager land (OLI: Operational Land Imager) and the Thermal Infrared Sensor (TIRS: Thermal InfraRed Sensor) and are available. This sensors collect image data, respectively, for the nine-band short wavelength in the range of 433-2300 nm and dual-band long wavelength thermal. Seven band of the nine band picked up by the sensor information of OLI to deal with sensors TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) in previous satellite Landsat compatible and two other band, the band of coastal water (433 to 453 nm) and Cirrus band (1360 to 1390 nm), short wave infrared provides to measure water quality and high thin clouds. Since OLI sensor in Landsat satellite 8 compared with other sensors to study OC have been allocated a much better spatial resolution can be more accurate to determine changes in OC. To evaluate the results of the image sensor MODIS (Moderate Resolution Imaging Spectroradiometer) at the same time satellite images Landsat 8 is used. The statistical parameters used in order to evaluate the performance of different algorithms, including root mean square error (RMSE) and coefficient of determination (R<sup>2</sup>), and on the basis of these parameters we choose the most appropriate algorithm for the area. Extracted results for implementing different OC algorithms clearly shows superiority of utilized method by R2=0.71 and RMSE=0.07.

Nutrient contents are important for plants. Lack of macronutrients causes plant damage. Several macronutrient deficiencies exhibit similar visual characteristics that are difficult for ordinary farmers to identify. Collaboration between Computer Vision technology and IoT has become a non-destructive method for nutrient monitoring and control, included in the hydroponic system. Computer vision plays a role in processing plant image data based on specific characteristics. However, the analysis of one characteristic cannot represent plant health. In addition, knowing the percentage of macronutrient deficiencies is also needed to support precision agriculture systems. Therefore, we propose a Multi Layer Perceptron architecture that can perform multi-tasks, namely, identification and estimation. In addition, the optimal architecture will also be sought based on the characteristics of the combination of three features in the form of texture, color, and leaf shape. Based on analysis and design, our proposed model has a high potential for identifying and estimating macronutrient deficiency at the same time as well and can be applied to support precision agriculture in Indonesia.

In this article, we present an adaptive color similarity function defined in a modified hue‐saturation‐intensity color space, which can be used directly as a metric to obtain pixel‐wise segmentation of color images among other applications. The color information of every pixel is integrated as a unit by an adaptive similarity function thus avoiding color information scattering. As a direct application we present an efficient interactive, supervised color segmentation method with linear complexity respect to the number of pixels of the input image. The process has three steps: (1) Manual selection of few pixels in a sample of the color to be segmented. (2) Automatic generation of the so called color similarity image (CSI), which is a gray level image with all the gray level tonalities associated with the selected color. (3) Automatic threshold of the CSI to obtain the final segmentation. The proposed technique is direct, simple and computationally inexpensive. The evaluation of the efficiency of the color segmentation method is presented showing good performance in all cases of study. A comparative study is made between the behavior of the proposed method and two comparable segmentation techniques in color images using (1) the Euclidean metric of thea* andb* color channels rejectingL* and (2) a probabilistic approach ona* andb* in the CIE L*a*b* color space. Our testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. It was obtained from the results that the color parametersa* andb* are not independent of the luminance parameterL* as one might initially assume in the CIE L*a*b* color space. We show that our solution improves the quality of the proposed color segmentation technique and its quick result is significant with respect to other solutions found in the literature. The method also gives a good performance in low chromaticity, gray level and low contrast images. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 156–172, 2017

Optical nerve stimulation (ONS) using infrared laser radiation is a technique developing as a potential alternative to electrical stimulation of nerve tissue. This preliminary study proposes and explores a computer simulation tool for numerically optimizing laser and surface scanning parameters including laser power and surface scanning speed to be used in laser scanning subsurface ONS. This tool consisted of three parts, including the Monte Carlo simulations for generating laser energy distribution in the tissue sample, the laser-scanning model by moving the heat source at the surface, and the thermal transfer simulations to calculate the tissue temperature. In the simulations, the laser wavelength of 1490 nm was used and the surface scan was performed on both x and y axes. In addition, the tissue model was constructed in such a way that the nerve tissue extends over the y-axis. As a result of calculations, the nerve tissue temperature map was produced as a function of laser power and surface scanning speed. According to the temperature map, the optimal laser power to reach the nerve temperature at 43 °C was estimated to be 15 mW at the scanning speeds of 1.2 mm/s in the x-axis and 1.0 mm/s in the y-axis. With further development this simulation tool may hold promise in the development of an optical stimulus device.

Surface scanning of the face of a suspect is presented as a way to better match the facial features with those of a perpetrator from CCTV footage. We performed a simple pilot study where we obtained facial surface scans of volunteers and then in blind trials tried to match these scans with 2D photographs of the faces of the volunteers. Fifteen male volunteers were surface scanned using a Polhemus FastSCAN Cobra Handheld Laser Scanner. Three photographs were taken of each volunteer's face in full frontal, profile and from above at an angle of 45 degrees and also 45 degrees laterally. Via special software (MIMICS and Photoshop) the surface scans were matched with the photographs in blind trials. The matches were graded as: a good fit; possible fit; and no fit. All the surface scans and photos were matched correctly, although one surface scan could be matched with two angled photographs, meaning that the discriminatory value was 86.7%. We also tested the surface scanner in terms of reliability in establishing point measures on skulls, and compared with physical measurements performed by calipers. The variation was on average 1 mm for five cranial measures. We suggest how surface scanning might be applied in forensic facial identification.

The integration of technological advancements has significantly impacted artificial intelligence (AI), enhancing the reliability of AI model outputs. This progress has led to the widespread utilization of AI across various sectors, including automotive, robotics, healthcare, space exploration, and defense. Today, air defense operations predominantly rely on laser designation. This process is entirely dependent on the capability and experience of human operators. Considering that UAV systems can have flight durations exceeding 24 h, this process is highly prone to errors due to the human factor. Therefore, the aim of this study is to automate the laser designation process using advanced deep learning algorithms on 3D point clouds obtained from different sources, thereby eliminating operator-related errors. As different data sources, dense 3D point clouds produced with photogrammetric methods containing color information, and point clouds produced with LiDAR systems were identified. The photogrammetric point cloud data were generated from images captured by the Akinci UAV’s multi-axis gimbal camera system within the scope of this study. For the point cloud data obtained from the LiDAR system, the DublinCity LiDAR dataset was used for testing purposes. The segmentation of point cloud data utilized the PointNet++ and RandLA-Net algorithms. Distinct differences were observed between the evaluated algorithms. The RandLA-Net algorithm, relying solely on geometric features, achieved an approximate accuracy of 94%, while integrating color features significantly improved its performance, raising its accuracy to nearly 97%. Similarly, the PointNet++ algorithm, relying solely on geometric features, achieved an accuracy of approximately 94%. Notably, the model developed as a unique contribution in this study involved enriching the PointNet++ algorithm by incorporating color attributes, leading to significant improvements with an approximate accuracy of 96%. The obtained results demonstrate a notable improvement in the PointNet++ algorithm with the proposed approach. Furthermore, it was demonstrated that the methodology proposed in this study can be effectively applied directly to data generated from different sources in aerial scanning systems.

Distinguishing false counts caused by surface microroughness and haze when measuring particles below 0.1 μm has become a significant concern for ultralarge scale integrated (ULSI) product yield. Initial results are presented from an industry‐wide, cross‐functional, SEMATECH task force exploring this issue by investigating both measurement capability and alternative detector strategies for laser surface scanners. Polystyrene latex (PSL) spheres and real‐world particles deposited under controlled conditions on 150 mm polished silicon wafers are used. Particle counting measurements taken at several facilities using the same model of laser surface scanner and the same instrument parameter settings are compared. Silicon particles are consistently sized incorrectly when the laser surface scanner is calibrated using standardized procedures utilizing PSL spheres. Measurements from similarly prepared samples using angle‐resolved scattering (ARS) to obtain scattering cross section as a function of angle are favorably compared to a numerical light scattering model. Modeling allows the comparison of data from instruments which measure ARS from individual particles as well as those which measure ARS from multiple identical particles. The improved signal‐to‐noise ratio of the multiple particle technique allows study of particle scattering at sizes below typical commercial equipment detection limits. A novel haze surface is described which can be used to verify the modeling of both research and commercial light scattering instruments.

To explore the potentiality of radiomics analysis, performed on Ga-DOTATOC and fluorine-18-fluorodeoxyglucose (F-FDG) PET/computed tomography (CT) images, in predicting tumour aggressiveness and outcome in patients candidate to surgery for pancreatic neuroendocrine neoplasms (PanNENs). Retrospective study including 61 patients who underwent Ga-DOTATOC and F-FDG PET/CT before surgery for PanNEN. Semiquantitative variables [SUVmax and somatostatin receptor density (SRD) for Ga-DOTATOC PET; SUVmax and MTV for F-FDG PET] and texture features [intensity variability, size zone variability (SZV), zone percentage, entropy; homogeneity, dissimilarity and coefficient of variation (Co-V)] have been analysed to evaluate their possible role in predicting tumour characteristics. Principal component analysis (PCA) was firstly performed and then multiple regression analyses were performed by using the extracted principal components. Regarding Ga-DOTATOC PET, SZV, entropy, intensity variability and SRD were predictive for tumour dimension. Regarding F-FDG PET, intensity variability, SZV, homogeneity, SUVmax and MTV were predictive for tumour dimension. Four principal components were extracted from PCA: PC1 correlated with all F-FDG variables, while PC2, PC3 and PC4 with Ga-DOTATOC variables. PC1 was the only significantly predicting angioinvasion (P = 0.0222); PC4 was the only one significantly predicting lymph nodal involvement (P = 0.0151). All principal components except PC4 significantly predicted tumour dimension (P <0.0001 for PC1, P = 0.0016 for PC2 and P < 0.0001 for PC3). Co-V from Ga-DOTATOC PET/CT was predictive of the outcome. Specific texture features derived from preoperative Ga-DOTATOC and F-FDG PET/CT could noninvasively predict specific tumour characteristics and patients' outcome, delineating the potential role of dual tracer technique and texture analysis in the risk assessment of patients with PanNENs.

Melanization is a common phenomenon in insects, and melanin synthesis is a conserved physiological process that occurs in epidermal cells. Moreover, a comprehensive understanding of the mechanisms of melanin synthesis influencing insect pigmentation are well-suited for investigating phenotype variation. The Asian multi-colored (Harlequin) ladybird beetle, Harmonia axyridis, exhibits intraspecific polymorphism based on relative levels of melanization. However, the specific characteristics of melanin synthesis in H. axyridis remains elusive. In this study, we performed gene-silencing analysis of the pivotal inverting enzyme, tyrosine hydroxylase (TH), and DOPA decarboxylase (DDC) in the tyrosine metabolism pathway to investigate the molecular and regulatory mechanism of melanin synthesis in H. axyridis. Using RNAi of TH and DDC genes in fourth instar larvae, we demonstrated that dopamine melanin was the primary contributor to the overall body melanization of H. axyridis. Furthermore, our study provides the first conclusive evidence that dopamine serves as a melanin precursor for synthesis in the early pupal stage. According to transcription factor Pannier, which is essential for the formation of melanic color on the elytra in H. axyridis, we further demonstrated that suppression of HaPnr can significantly decrease expression levels of HaTH and HaDDC. These results in their entirety lead to the conclusion that transcription factor Pannier can regulate dopamine melanin synthesis in the dorsal elytral epidermis of H. axyridis.

Objective: To compare the health status (stress, depressive symptoms and sleep quality), the resilience and quality of life in first and fourth year nursing students.Methods: This is a cross-sectional research conducted in 2016 with 86 students enrolled in first and fourth years of the nursing degree. We applied the instrument for Assessment of Stress in Nursing Students, the Center for Epidemiologic Studies Depression Scale, Pittsburg Sleep Quality Index, Wagnild and Young’s Resilience Scale; and the WHOQOL-BREF. ANOVA (Test F) was applied for data analysis.Results and conclusions: A total of 49 first-year and 37 fourth-year students were sampled for this study. Fourth- year nursing students showed higher levels of stress, lower intensity of depressive symptoms and higher quality of life and resilience levels. The poor sleep quality was prevalent in both groups. Conclusion: although the nursing education potentially contributes for students’ sickness, the experiences lived in this period may strength the resilience skills.Conclusions: Video indexing and retrieval are accomplished by using hashing and $k$-d tree methods, while visual signatures containing color, shape and texture information are estimated for the key-frames, by using image and frequency domain techniques. Experimental results with the dataset of a multimedia information system especially developed for managing television broadcast archives demonstrate that our approach works efficiently, retrieving videos in 0.16 seconds on average and achieving recall, precision and F1 measure values, as high as 0.76, 0.97 and 0.86 respectively.

This work addresses the development of a unified approach to content-based indexing and retrieval of digital videos from television archives. The proposed approach has been designed to deal with arbitrary television genres, making it suitable for various applications. To achieve this goal, the main steps of a content-based video retrieval system are addressed in this work, namely: video segmentation, key-frame extraction, content-based video indexing and the video retrieval operation itself. Video segmentation is addressed as a typical TV broadcast structuring problem, which consists in automatically determining the boundaries of each broadcasted program (like movies, news, among others) and inter-program (for instance, commercials). Specifically, to segment the videos, Electronic Program Guide (EPG) metadata is combined with the detection of two special cues, namely, audio cuts (silence) and dark monochrome frames. On the other hand, a color histogram-based approach performs key-frame extraction. Video indexing and retrieval are accomplished by using hashing and k-d tree methods, while visual signatures containing color, shape and texture information are estimated for the key-frames, by using image and frequency domain techniques. Experimental results with the dataset of a multimedia information system especially developed for managing television broadcast archives demonstrate that our approach works efficiently, retrieving videos in 0.16 seconds on average and achieving recall, precision and F1 measure values, as high as 0.76, 0.97 and 0.86 respectively.

Intuitive Facial Imaging Method for Evaluation of Postoperative Swelling: A Combination of 3-Dimensional Computed Tomography and Laser Surface Scanning in Orthognathic Surgery

The use of laser technology to scan hardwood log surfaces for defects holds great promise for improving processing efficiency and the value and volume of lumber produced. External and internal defect detection to optimize hardwood log and lumber processing is one of the top four technological needs in the nation’s hardwood industry. The location, type, and severity of defects on hardwood logs are the key indicators of log quality and value. These visual cues provide information about internal log characteristics and products for which the log is suitable. We scanned 162 logs with a high-resolution industrial four-head laser surface scanner. The resulting data sets contain hundreds of thousands of three-dimensional coordinate points. The size of the data and noise presented special problems during processing. Robust regression models were used to fit geometric shapes to the data. The estimated orthogonal distances between the fitted model and the log surface are converted to a two-dimensional image to facilitate defect detection. Using robust regression methods and standard image processing tools we have demonstrated that severe surface defects on hardwood logs can be detected using height and contour analyses of three-dimensional laser scan data.

Relevance. The guided bone regeneration (GBR) technique during dental implant treatment, if alveolar bone/part of the jaws is lost, stands out for its effective potential, the main factor of which is the possibility of the necessary bone volume restoration having morphofunctional signs of organotypicity. The prognosis effectiveness of such reconstruction outcome directly depends on the wound process characteristics. The variability of the latter, subject to an unbiased quantitative assessment, can be essential for predictive analysis and prognosis.Aim. The study aimed to develop a technique for quantitative assessment of surgical wound healing (exemplified with an extracted tooth socket), described in the first part of the publication.Materials and methods. The clinical study involved 20 patients of both sexes diagnosed with the condition after dental implantation and reconstruction of the alveolar bone volume using the GBR technique modified by the formation of a situational vicryl framework (SVF). We determined the following parameters: independent and dependent variables, with their indirect mutually dependent influence on a favourable, compromised or unfavourable treatment outcome, based on the wound healing process follow-up and assessment of immediate results in the early, middle and late postoperative period − up to 1 month, and the analysis of delayed – from 1 month to 1 year, and long-term – over one year. These variables are characterized as quantitative indicators – biomarkers, processed by the correlation and regression analysis using the Gretl statistical package.Results. The study revealed a direct correlation regarding all parameters as independent (factor) and dependent (effect) variables necessary for quantifying the wound healing process variability. The created linear regression model confirmed the empirically observed and theoretically substantiated idea of the reciprocal influence of normal and pathological variations of wound healing process on an outcome of dental implantation in the reconstruction of the alveolar bone defect. The correlation and regression analysis for an unbiased quantitative assessment of surgical wound healing demonstrated the statistically explicable behaviour of dependent variables at a 95% le vel.Conclusion. The correlation and regression analysis performed using the presented data indicates a direct correlation between the parameters of a favourable, compromised and unfavourable outcome of dental implantation during the reconstruction of the alveolar bone volume using the GBR technique modified by the formation of SVF and variables expressed by biomarkers, such as the initial complexity of clinical conditions, duration of surgery, mechanical load of the wound, bio-reactive properties of the additional materials required in the w ound.