New approaches for low phototoxicity imaging of living cells and tissues.

Fluorescence microscopy is a powerful tool used in scientific and medical research, but it is inextricably linked to phototoxicity. Neglecting phototoxicity can lead to erroneous or inconclusive results. Recently, several reports have addressed this issue, but it is still underestimated by many researchers, even though it can lead to cell death. Phototoxicity can be reduced by appropriate microscopic techniques and carefully designed experiments. This review focuses on recent strategies to reduce phototoxicity in microscopic imaging of living cells and tissues. We describe digital image processing and new hardware solutions. We point out new modifications of microscopy methods and hope that this review will interest microscopy hardware engineers. Our aim is to underscore the challenges and potential solutions integral to the design of microscopy systems. Simultaneously, we intend to engage biologists, offering insight into the latest technological advancements in imaging that can enhance their understanding and practice.

Soft Contact Lens Engraving Characterization by Wavefront Holoscopy.

Permanent engravings on contact lenses provide information about the manufacturing process and lens positioning when they are placed on the eye. The inspection of their morphological characteristics is important, since they can affect the user's comfort and deposit adhesion. Therefore, an inverted wavefront holoscope (a lensless microscope based on Gabor's principle of in-line digital holography) is explored for the characterization of the permanent marks of soft contact lenses. The device, based on an in-line transmission configuration, uses a partially coherent laser source to illuminate the soft contact lens placed in a cuvette filled with a saline solution for lens preservation. Holograms were recorded on a digital sensor and reconstructed by back propagation to the image plane based on the angular spectrum method. In addition, a phase-retrieval algorithm was used to enhance the quality of the recovered images. The instrument was experimentally validated through a calibration process in terms of spatial resolution and thickness estimation, showing values that perfectly agree with those that were theoretically expected. Finally, phase maps of different engravings for three commercial soft contact lenses were successfully reconstructed, validating the inverted wavefront holoscope as a potential instrument for the characterization of the permanent marks of soft contact lenses. To improve the final image quality of reconstructions, the geometry of lenses should be considered to avoid induced aberration effects.

HeLa cell segmentation using digital image processing.

Computational cell segmentation is a vital area of research, particularly in the analysis of images of cancer cells. The use of cell lines, such as the widely utilized HeLa cell line, is crucial for studying cancer. While deep learning algorithms have been commonly employed for cell segmentation, their resource and data requirements can be impractical for many laboratories. In contrast, image processing algorithms provide a promising alternative due to their effectiveness and minimal resource demands. This article presents the development of an algorithm utilizing digital image processing to segment the nucleus and shape of HeLa cells. The research aims to segment the cell shape in the image center and accurately identify the nucleus. The study uses and processes 300 images obtained from Serial Block-Face Scanning Electron Microscopy (SBF-SEM). For cell segmentation, the morphological operation of erosion was used to separate the cells, and through distance calculation, the cell located at the center of the image was selected. Subsequently, the eroded shape was employed to restore the original cell shape. The nucleus segmentation uses parameters such as distances and sizes, along with the implementation of verification stages to ensure accurate detection. The accuracy of the algorithm is demonstrated by comparing it with another algorithm meeting the same conditions, using four segmentation similarity metrics. The evaluation results rank the proposed algorithm as the superior choice, highlighting significant outcomes. The algorithm developed represents a crucial initial step towards more accurate disease analysis. In addition, it enables the measurement of shapes and the identification of morphological alterations, damages, and changes in organelles within the cell, which can be vital for diagnostic purposes.

A preliminary study of combustion flame by digital image processing and residue for fireworks flash powder.

Fireworks play a vital role in festive celebrations and entertainment. These fireworks industries are administered by the Petroleum and Explosives Safety Organisation (PESO), Nagpur, Government of India. Even though PESO prescribed the standard composition for various fireworks products, concerns persist regarding the potential deviation from these standards, particularly in the pursuit of increased noise levels to attract consumers. This change in the burning and reactive properties of chemicals could potentially lead to accidents and environmental pollution. The current work investigated the combustion characteristics of two unknown market flash powder samples and a PESO standard sample. The samples were openly ignited in a controlled atmosphere, and video recordings of the resulting chemical flames were analyzed to determine fire area, flame intensity, and motion magnitude. The residues were also collected for EDAX and SEM analysis. Fire pixels from video frames are segmented to create a comprehensive database detailing flame area and motion magnitude under different circumstances. From the flame area profile, motion magnitude and SEM/EDAX analysis, it is found that PESO standard sample combustion is better than the unknown flash powder combustion.

An improved corner dealiasing and recognition algorithm for 2D Wadell roundness computation.

This paper optimizes the 2D Wadell roundness calculation of particles based on digital image processing methods. An algorithm for grouping corner key points is proposed to distinguish each independent corner. Additionally, the cyclic midpoint filtering method is introduced for corner dealiasing, aiming to mitigate aliasing issues effectively. The relationships between the number of corner pixels (m), the central angle of the corner (α) and the parameter of the dealiasing degree (n) are established. The Krumbein chart and a sandstone thin section image were used as examples to calculate the 2D Wadell roundness. A set of regular shapes is calculated, and the error of this method is discussed. When α ≥ 30°, the maximum error of Wadell roundness for regular shapes is 5.21%; when 12° ≤ α < 30°, the maximum error increases. By applying interpolation to increase the corner pixels to the minimum number (m0) within the allowable range of error, based on the α-m0 relational expression obtained in this study, the error of the corner circle can be minimized. The results indicate that as the value of m increases, the optimal range interval for n also widens. Additionally, a higher value of α leads to a lower dependence on m. The study's results can be applied to dealiasing and shape analysis of complex closed contours.

Optimized Electrophoretic Deposition of Chitosan/Mesoporous Glass Nanoparticles with Gentamicin on Titanium Implants: Enhancing Hemocompatibility and Antibacterial Activity.

Titanium-based implants have long been studied and used for applications in bone tissue engineering, thanks to their outstanding mechanical properties and appropriate biocompatibility. However, many implants struggle with osseointegration and attachment and can be vulnerable to the development of infections. In this work, we have developed a composite coating via electrophoretic deposition, which is both bioactive and antibacterial. Mesoporous bioactive glass particles with gentamicin were electrophoretically deposited onto a titanium substrate. In order to validate the hypothesis that the quantity of particles in the coatings is sufficiently high and uniform in each deposition process, an easy-to-use image processing algorithm was designed to minimize human dependence and ensure reproducible results. The addition of loaded mesoporous particles did not affect the good adhesion of the coating to the substrate although roughness was clearly enhanced. After 7 days of immersion, the composite coatings were almost dissolved and released, but phosphate-related compounds started to nucleate at the surface. With a simple and low-cost technique like electrophoretic deposition, and optimized stir and suspension times, we were able to synthesize a hemocompatible coating that significantly improves the antibacterial activity when compared to the bare substrate for both Gram-positive and Gram-negative bacteria.

A Pilot Clinical and Technical Validation of an Immersive Virtual Reality Platform for 3D Anatomical Modeling and Contouring in Support of Surgical and Radiation Oncology Treatment Planning.

The aim of this study was to validate a novel medical virtual reality (VR) platform used for medical image segmentation and contouring in radiation oncology and 3D anatomical modeling and simulation for planning medical interventions, including surgery. The first step of the validation was to verify quantitatively and qualitatively that the VR platform can produce substantially equivalent 3D anatomical models, image contours, and measurements to those generated with existing commercial platforms. To achieve this, a total of eight image sets and 18 structures were segmented using both VR and reference commercial platforms. The image sets were chosen to cover a broad range of scanner manufacturers, modalities, and voxel dimensions. The second step consisted of evaluating whether the VR platform could provide efficiency improvements for target delineation in radiation oncology planning. To assess this, the image sets for five pediatric patients with resected standard-risk medulloblastoma were used to contour target volumes in support of treatment planning of craniospinal irradiation, requiring complete inclusion of the entire cerebral-spinal volume. Structures generated in the VR and the commercial platforms were found to have a high degree of similarity, with dice similarity coefficient ranging from 0.963 to 0.985 for high-resolution images and 0.920 to 0.990 for lower resolution images. Volume, cross-sectional area, and length measurements were also found to be in agreement with reference values derived from a commercial system, with length measurements having a maximum difference of 0.22 mm, angle measurements having a maximum difference of 0.04°, and cross-sectional area measurements having a maximum difference of 0.16 mm2. The VR platform was also found to yield significant efficiency improvements, reducing the time required to delineate complex cranial and spinal target volumes by an average of 50% or 29 min.

An eco-friendly approach for analysing sugars, minerals, and colour in brown sugar using digital image processing and machine learning.

Brown sugar is a natural sweetener obtained by thermal processing, with interesting nutritional characteristics. However, it has significant sensory variability, which directly affects product quality and consumer choice. Therefore, developing rapid methods for its quality control is desirable. This work proposes a fast, environmentally friendly, and accurate method for the simultaneous analysis of sucrose, reducing sugars, minerals and ICUMSA colour in brown sugar, using an innovative strategy that combines digital image processing acquired by smartphone cell with machine learning. Data extracted from the digital images, as well as experimentally determined contents of the physicochemical characteristics and elemental profile were the variables adopted for building predictive regression models by applying the kNN algorithm. The models achieved the highest predictive capacity for the Ca, ICUMSA colour, Fe and Zn, with coefficients of determination (R2) ≥ 92.33 %. Lower R2 values were observed for sucrose (81.16 %), reducing sugars (85.67 %), Mn (83.36 %) and Mg (86.97 %). Low data dispersion was found for all the predictive models generated (RMSE < 0.235). The AGREE Metric assessed the green profile and determined that the proposed approach is superior in relation to conventional methods because it avoids the use of solvents and toxic reagents, consumes minimal energy, produces no toxic waste, and is safer for analysts. The combination of digital image processing (DIP) and the kNN algorithm provides a fast, non-invasive and sustainable analytical approach. It streamlines and improves quality control of brown sugar, enabling the production of sweeteners that meet consumer demands and industry standards.

Low-Temperature Crack Resistance of High-Content Rubber-Powder-Modified Asphalt Mixture under Freeze-Thaw Cycles.

In order to study the modification mechanisms of a warm-mixing agent and high dosage on rubber-powder-modified asphalt, as well as the influence of salt freeze-thaw cycling on the mechanism of warm-mixed high-dosage-rubber-powder-modified asphalt, macro- and micro-experimental methods were used to analyze the low-temperature crack resistance performance of six types of rubber-powder-modified asphalt mixtures under salt freeze-thaw cycling. By using digital image processing (DIC) technology to record and analyze the loading processes of specimens in semicircular three-point bending (SCB) tests, combined with atomic force microscopy (AFM) tests, the low-temperature crack resistance of the asphalt mixtures was explored, and it was inferred that the micro-mechanical performance indicators of the asphalt were correlated with the low-temperature crack resistance performance indicators of the asphalt mixtures. The results indicate that the salt solution caused greater damage to the asphalt than water. The addition of more rubber powder improved the low-temperature cracking resistance of the asphalt mixtures. There was a significant correlation between the micro-mechanical properties of the asphalt and the low-temperature crack resistance of the asphalt mixtures, and a dynamic mechanical thermal analyzer (DMT) showed a stronger correlation with the strain derivative (E'(t)) than the adhesion force index. The SDYK-type warm-mixing agent had a better effect on the low-temperature cracking resistance of the asphalt mixtures than the EM-type warm-mixing agent.

Study on the damage and seepage characteristics of water-saturated coal by microwave cycling.

To investigate the extent of damage and seepage characteristics of water-saturated coal samples after subjecting them to microwave cycling. The microwave equipment was used to process the coal samples by microwave cycling. The non-contact digital image processing technology and acoustic emission system were used to carry out the triaxial loading experimental study of the coal samples to obtain the mechanical parameter characteristics, energy evolution pattern, acoustic emission information and permeability characteristics of coal samples under different microwave cycle times. The results of the study show that: With the increase in the number of microwave cycles, dense grid-loaded cracks gradually appeared on the surface of the coal samples, the triaxial partial stresses of the coal samples decreased, and the strains also decreased, and the modulus of elasticity and Poisson's ratio also decreased; In the densification stage stage, the dissipated energy is higher than the elastic energy, and as the elastic stage proceeds, the elastic energy gradually reverses to exceed the dissipated energy, and the total energy and elastic energy of the coal samples decrease with the increase in the number of cycles, and the dissipated energy rises; Coal samples produce a large number of fissures due to the increase in the number of microwave cycles, the more frequent the fissure activity during the loading process, the acoustic emission amplitude and ringing count scattering points all become dense with the increase in the number of cycles, and the data increase; Initial permeability, destructive permeability and average permeability were all increased, microwave treatment has a better effect of permeability enhancement, the permeability of the treated coal samples was changed from low permeability to medium permeability, and the permeability enhancement was the largest in 6 cycles, and the permeability was increased by 7.18 times. This article explores the damage condition of water-saturated coal samples under microwave cycling treatment. Then, it explores the effect of microwave cycling on the permeability enhancement of the coal body, which provides a new method for exploring the gas permeability enhancement and extraction of low-permeability coal samples underground.

Determination of early bone metastasis on Bone Scans Using the Gray Levels Histogram / Búsqueda de metástasis ósea temprana en gammagramas óseos usando el histograma de tonos de gris

ABSTRACT The aim of this paper is to show a technique to speed up the interpretation of bone scans in order to determine the presence of early bone metastasis. This is done using the gray levels histogram of the region of interest. The technique is intended to assist in the bone scans interpretation in order to provide a successful diagnosis. During the analysis, three types of histograms were observed on the regions of interest. If the histogram is narrow and shifted toward the origin, the bone scan is free of metastasis. If it is shifted to the right and slightly broadened, indicates the presence of a bone anomaly different from a metastasis. On the other hand, if the histogram is more broadened and shifted to the right, is suggests the presence of metastasis. This histogram is characterized by displaying small curls on the right side providing information about the metastatic disease stage, which could be low-amplitude peaks and have a short length, if the metastasis is in early stage, or high-amplitude peaks and a long length, if is advanced. Finally, the analyzed region is displayed in false color considering the minimum gray levels observed in the histogram.

Visualizando Gammagramas Óseos en Colores / Displaying Bone Scans in Color

Resumen En este trabajo se describe el uso de un software para detectar metástasis óseas en gammagramas cuyo funcionamiento está basado en un método cuantitativo. En la investigación se incluyeron cuarenta y tres gammagramas óseos de pacientes con cáncer de próstata, los cuales fueron previamente analizados visualmente por tres especialistas y su diagnóstico se tomó como referencia. Debido a que no todos los huesos de un paciente muestran los mismos tonos de gris, cada uno de los gammagramas se segmentó para su análisis en cuatro regiones: cráneo, hombros, tórax y pelvis. La segmentación se hizo con el fin de obtener intervalos de tonos de gris en cada una de las regiones empleando un proceso estadístico. Para ello se calcularon la media y la desviación estándar de cuatro muestras que contenían cada una cuarenta y tres regiones. Tomando en consideración los intervalos, siete colores fueron asignados a cada región. Mediante los colores fue posible diferenciar los casos sanos e infiltrados en cada región, lo que facilita el diagnóstico. Se muestran ejemplos de los resultados en cada una de las regiones.

Abstract The use of software based on a quantitative method for detecting skeletal metastasis in bone scans is presented. Forty-three bone scans of patients with prostate cancer were previously analyzed visually by three specialists and their diagnosis were taken as a reference. Later, these scans were analyzed by segmenting them into four regions: skull, shoulders, chest, and pelvis. The segmentation was made to obtain intervals of gray levels for each of the regions using a statistical process. Tríese values were found by calculating the mean and standard deviation of four samples containing each forty-three regions. Using these intervals, seven colors were assigned to each region. By means of the colors it is possible to differentiate healthy and infiltrated cases in each region, which may facilitate the diagnosis. Examples of results in each region are shown.

Automatic segmentation and classification of blood components in microscopic images using a fuzzy approach

INTRODUCTION: Automatic detection of blood components is an important topic in the field of hematology. Segmentation is an important step because it allows components to be grouped into common areas and processed separately. This paper proposes a method for the automatic segmentation and classification of blood components in microscopic images using a general and automatic fuzzy approach. METHODS: During pre-processing, the supports of the fuzzy sets are automatically calculated based on the histogram peaks in the green channel of the RGB image and the Euclidean distance between the leukocyte nuclei centroids and the remaining pixels. During processing, fuzzification associates the degree of pertinence of the gray level of each pixel in the regions defined in the histogram with the proximity of the leukocyte nucleus centroid closest to the pixel. The fuzzy rules are then applied, and the image is defuzzified, resulting in the classification of four regions: leukocyte nuclei, leukocyte cytoplasm, erythrocytes and blood plasma. In post-processing, false positives are reduced and the leukocytes (including the nucleus and cytoplasm), erythrocytes and blood plasma are segmented. RESULTS: A total of 530 microscopic images of blood smears were processed, and the results were compared with the results of manual segmentation by experts and the accuracy rates of other approaches. CONCLUSION: The method demonstrated average accuracy rates of 97.31% for leukocytes, 95.39% for erythrocytes and 95.06% for blood plasma, avoiding the limitations found in the literature and contributing to the practice of the segmentation of blood components.

Leukocytes Detection, Classification and Counting in Smears of Peripheral Blood / Detección, Clasificación y Conteo de Leucocitos en Frotis de Sangre Periférica

Using the k-NN classifier in combination with the first Minkowski metric, in addition to techniques of digital image processing, we developed a computational system platform-independent, which is able to identify, to classify and to count five normal types of leukocytes: neutrophils, eosinophils, basophils, monocytes and lymphocytes. It is important to emphasize that this work does not attempt to diferentiate between smears of leukocytes coming from healthy and sick people; this is because most diseases produce a change in the differential count of leukocytes rather than in theirs forms. In the other side, the system could be used in emerging areas such as the topographic hematology and the chronobiology.

Mediante un clasificador k-NN en combinación con la primera métrica de Minkowski y técnicas de procesamiento digital de imágenes, se desarrolló un sistema computacional independiente de la plataforma, capaz de identificar, clasificar y contar cinco formas normales de leucocitos: neutrófilos, eosinófilos, basófilos, monocitos y linfocitos. Es importante enfatizar que este trabajo no intenta diferenciar entre muestras de leucocitos provenientes de gente sana y enferma, debido a que la mayoría de las enfermedades se detectan principalmente por un cambio en el conteo diferencial de leucocitos más que por cambios en su forma. Finalmente, el contador de leucocitos puede ser usado en áreas emergentes como la hematología topográfica y la cronobiología.

Method for automatic detection of wheezing in lung sounds

The present report describes the development of a technique for automatic wheezing recognition in digitally recorded lung sounds. This method is based on the extraction and processing of spectral information from the respiratory cycle and the use of these data for user feedback and automatic recognition. The respiratory cycle is first pre-processed, in order to normalize its spectral information, and its spectrogram is then computed. After this procedure, the spectrogram image is processed by a two-dimensional convolution filter and a half-threshold in order to increase the contrast and isolate its highest amplitude components, respectively. Thus, in order to generate more compressed data to automatic recognition, the spectral projection from the processed spectrogram is computed and stored as an array. The higher magnitude values of the array and its respective spectral values are then located and used as inputs to a multi-layer perceptron artificial neural network, which results an automatic indication about the presence of wheezes. For validation of the methodology, lung sounds recorded from three different repositories were used. The results show that the proposed technique achieves 84.82% accuracy in the detection of wheezing for an isolated respiratory cycle and 92.86% accuracy for the detection of wheezes when detection is carried out using groups of respiratory cycles obtained from the same person. Also, the system presents the original recorded sound and the post-processed spectrogram image for the user to draw his own conclusions from the data.