Digital Image Research Articles

Prediction of Mismatch Repair Status in Endometrial Cancer from Histological Slide Images Using Various Deep Learning-Based Algorithms

The application of deep learning algorithms to predict the molecular profiles of various cancers from digital images of hematoxylin and eosin (H&E)-stained slides has been reported in recent years, mainly for gastric and colon cancers. In this study, we investigated the potential use of H&E-stained endometrial cancer slide images to predict the associated mismatch repair (MMR) status. H&E-stained slide images were collected from 127 cases of the primary lesion of endometrial cancer. After digitization using a Nanozoomer virtual slide scanner (Hamamatsu Photonics), we segmented the scanned images into 5397 tiles of 512 × 512 pixels. The MMR proteins (PMS2, MSH6) were immunohistochemically stained, classified into MMR proficient/deficient, and annotated for each case and tile. We trained several neural networks, including convolutional and attention-based networks, using tiles annotated with the MMR status. Among the tested networks, ResNet50 exhibited the highest area under the receiver operating characteristic curve (AUROC) of 0.91 for predicting the MMR status. The constructed prediction algorithm may be applicable to other molecular profiles and useful for pre-screening before implementing other, more costly genetic profiling tests.

Mesoscopic precursor anomaly characteristics of red sandstone under uniaxial compression after cyclic high-temperatures

The deformation and failure characteristics of rock caused by periodic changes of temperature are not only significantly different from that under normal temperature, but also significantly different from that produced by a single high-temperature event. Here, using a three-dimensional digital image correlation (3D-DIC) integrated test system, mechanical and micro-damage tests of red sandstone are performed under cycles of high-temperature and natural-cooling with a temperature range of 25°C to 300°C and the number of cycles varying between 0 and 25. Macroscopic, mesoscopic and microscopic characteristics such as rock strength, deformation, strain field and fracture morphology are examined and their evolution with the number of heating-cooling cycles is discussed. Using the time-history evolution of the differentiation rate of the precursor anomaly (En), the characteristic coefficient (K) and the damage variable (DF), three kinds of characteristic time points are put forward to quantify the incubation and development time of precursor abnormal characteristics and are used to divide the whole process of deformation and failure. These time points are the precursor abnormal differentiation time (PADT) tE, the precursor abnormal startup time (PAST) tK, and the precursor abnormal failure time (PAFT) tD. Test results show that: (1) The action of cyclic high-temperature natural-cooling not only shows the strengthening effect of temperature, but also shows the strengthening effect of a certain number of cycles. (2) The evolution sequence of the three precursor abnormal characteristic moments is PADT, PAST and PAFT in turn, and this sequence can intuitively describe the whole complex process of rock deformation and failure, and there are wave peaks in the evolution curve with the number of high-temperature cycle. (3) The effect of high temperature (i.e., 300°C) cyclic heating on rock properties is similar to the threshold effect of a single high-temperature heating event.

Identification of Novel Loci Precisely Modulating Pre-Harvest Sprouting Resistance and Red Color Components of the Seed Coat in T. aestivum L.

The association between pre-harvest sprouting (PHS) and seed coat color has long been recognized. Red-grained wheats generally exhibit greater PHS resistance compared to white-grained wheat, although variability in PHS resistance exists within red-grained varieties. Here, we conducted a genome-wide association study on a panel consisting of red-grained wheat varieties, aimed at uncovering genes that modulate PHS resistance and red color components of seed coat using digital image processing. Twelve loci associated with PHS traits were identified, nine of which were described for the first time. Genetic loci marked by SNPs AX-95172164 (chromosome 1B) and AX-158544327 (chromosome 7D) explained approximately 25% of germination index variance, highlighting their value for breeding PHS-resistant varieties. The most promising candidate gene for PHS resistance was TraesCS6B02G147900, encoding a protein involved in aleurone layer morphogenesis. Twenty-six SNPs were significantly associated with grain color, independently of the known Tamyb10 gene. Most of them were related to multiple color characteristics. Prioritization of genes within the revealed loci identified TraesCS1D03G0758600 and TraesCS7B03G1296800, involved in the regulation of pigment biosynthesis and in controlling pigment accumulation. In conclusion, our study identifies new loci associated with grain color and germination index, providing insights into the genetic mechanisms underlying these traits.

Construction of a painting image classification model based on AI stroke feature extraction

Abstract A large number of digital painting image resources cannot be directly converted into electronic form due to their differences in painting techniques and poor preservation of paintings. Moreover, the difficulty of extracting classification features can also lead to the consumption of human time and misclassification problems. The aim of this research is to address the challenges of converting various digital painting image resources into electronic form and the difficulties of accurately extracting classification features. The goal is to improve the usefulness and accuracy of painting image classification. Converting various digital painting image resources directly into electronic format and accurately extracting classification features are challenging due to differences in painting techniques and painting preservation, as well as the complexity of accurately extracting classification features. Overcoming these adjustments and improving the classification of painting features with the help of artificial intelligence (AI) techniques is crucial. The existing classification methods have good applications in different fields. But their research on painting classification is relatively limited. In order to better manage the painting system, advanced intelligent algorithms need to be introduced for corresponding work, such as feature recognition, image classification, etc. Through these studies, unlabeled classification of massive painting images can be carried out, while guiding future research directions. This study proposes an image classification model based on AI stroke features, which utilizes edge detection and grayscale image feature extraction to extract stroke features; and the convolutional neural network (CNN) and support vector machine are introduced into image classification, and an improved LeNet-5 CNN is proposed to achieve comprehensive assurance of image feature extraction. Considering the diversity of painting image features, the study combines color features with stroke features, and uses weighted K-means clustering algorithm to extract sample features. The experiment illustrates that the K-CNN hybrid model proposed in the study achieved an accuracy of 94.37% in extracting image information, which is higher than 78.24, 85.69, and 86.78% of C4.5, K-Nearest Neighbor (KNN), and Bi directional Long short-term Memory (BiLSTM) algorithms. In terms of image classification information recognition, the algorithms with better performance from good to poor are: the mixed model > BiLSTM > KNN > C4.5 model, with corresponding accuracy values of 0.938, 0.897, 0.872, and 0.851, respectively. And the number of fluctuation nodes in the mixed model is relatively small. And the sample search time is significantly shorter than other comparison algorithms, with a maximum recognition accuracy of 92.64% for the style, content, color, texture, and direction features of the image, which can effectively recognize the contrast and discrimination of the image. This method effectively provides a new technical means and research direction for digitizing image information.

Fractional Fourier-transform off-axis digital holographic imaging

A fractional Fourier-transform digital holographic imaging method with resolution enhancement features is presented. In an optical configuration, an extended fractional Fourier-transform optical setup is set in the object arm of an off-axis digital holographic recording system to record a fractional Fourier-transform hologram via the optical interference of the fractional Fourier-transform wavefront of an object wave with a reference wave. For reconstruction imaging, the reconstruction approach for fractional Fourier-transform holograms is given. In the experiment, the fractional Fourier-transform digital holograms are recorded under the different recording parameters, and their amplitude images are effectively reconstructed. The imaging results demonstrate that the reconstruction-imaging resolution of fractional-order Fourier-transform holograms is obviously enhanced compared to that of conventional image-plane holograms. The presented fractional Fourier-transform digital holographic imaging with resolution enhancement and optical configuration flexibility provides, to our knowledge, a novel way for off-axis digital holographic imaging.

Enchancing Lung Disease Classification through K-Means Clustering, Chan-Vese Segmentation, and Canny Edge Detection on X-Ray Segmented Images

The lungs are one of the vital organs in the human body. Not only play a role in the respiratory system, the lungs are also responsible for the human circulatory system. Supporting examinations can also facilitate medical workers in determining the diagnosis. Usually a lung examination is complemented by a chest X-ray examination procedure. This examination aims to see directly and assess the severity of lung conditions. With current technological advances, image analysis can be done easily. Through digital image processing methods, information can be obtained from images that can be used for analysis as a support for diagnoses in the world of health. Image segmentation is a method in which digital images are divided into several segments or subgroups based on the characteristics of the pixels in the image. In this study, clustering with the K-Means method will be carried out on the results of segmentation of x-ray images of lung diseases, namely Covid-19, Tuberculosis, and Pneumonia. The segmentation method that will be implemented is the Chan-Vese Method and the Canny Edge Detection Method. This research shows that the results of the accuracy of applying the K-Means Clustering method to Chan-Vese and Canny Edge-Based Image Segmentation are 80%.

A Novel Approach to Optimizing Convolutional Neural Networks for Improved Digital Image Segmentation

To divide a digital image into individual parts that share similar characteristics is known as digital image segmentation, and it is a vital research subject in the field of computer vision. Object recognition, medical imaging, surveillance, and video processing are just a few of the many real-world contexts where this study could prove useful. While digital image segmentation research has come a long way, there are still certain obstacles to overcome. Segmentation algorithms frequently encounter challenges in achieving both accuracy and efficiency when confronted with intricate settings, noisy pictures, or fluctuating lighting conditions. The absence of established evaluation standards adds complexity to the process of performing equitable comparisons among different segmentation methodologies. Due to the subjective nature of photo segmentation, attaining consistent results among specialists can be challenging. The integration of machine learning and deep neural networks into segmentation algorithms has introduced new challenges, including the need for large amounts of annotated data and the interpretability of the outcomes. Given these challenges, the objective of this study is to enhance the segmentation model. To this end, this research suggests a model of convolutional neural networks that is optimal for digital picture segmentation. The model is based on a dense convolution neural network, and it incorporates a transfer learning technique to significantly boost the model’s robustness and the quality of picture segmentation. The model’s adaptability to new datasets is improved by the incorporation of a transfer learning method. As demonstrated by experimental results on two publicly available datasets, the suggested methodology considerably enhances the resilience of digital picture segmentation.

The use of phototherapy with different wavelengths in the treatment of Androgenetic Alopecia

Introduction: Phototherapy has been identified as a safe, non-invasive, and promising resource for the treatment of Androgenetic Alopecia (AGA). The aim of this study was to evaluate the effects of phototherapy on the treatment of AGA using two different wavelengths: red and blue. Both red and blue light, under the parameters tested in this study, allowed an increase in hair density, a reduction in the central parting, and promoted an increase in the number of terminal hairs after the treatments, as well as a reduction in vellus hairs. Methodology: Ten volunteers were selected (GLV n=5) and (GLA N=5), all diagnosed with AGA and approved by the ethics committee with opinion number 5,761,579. Interventions were carried out using Vênus equipment (MMOptics, São Carlos, Brazil) with Group GLV λ=660nm, 4J and Group GLA λ=440 nm, 4J. The interventions were performed once a week, for 90 days, totaling 12 treatment sessions. Hair progression was assessed by photographic record and the ratio of vellus to terminal hairs was performed by digital trichoscopy imaging (Dino Lite; New Taipei City, Taiwan, China). Results and Conclusion: Both red and blue light, under the parameters tested in this study, allowed an increase in hair density, a reduction in the central parting, and promoted an increase in the number of terminal hairs after the treatments, as well as a reduction in vellus hairs.

Radial basis point interpolation for strain field calculation in digital image correlation

In order to extract smooth and accurate strain fields from the noisy displacement fields obtained by digital image correlation (DIC), a point interpolation meshless (PIM) method with a radial basis function (RBF) is introduced for full-field strain calculation, which overcomes the problems of slow calculation speed and unstable matrix inverse calculation of the element-free Galerkin method (EFG). The radial basis point interpolation method (RPIM) with three different radial basis functions and the moving least squares (MLS) and pointwise least squares (PLS) methods are compared by analyzing and validating the strain fields with high-strain gradients in simulation experiments. The results indicate that the RPIM is nearly 80% more computationally efficient than the MLS method when a larger support domain is used, and the efficiency of the RPIM is nearly 26% higher than that of the MLS method when a smaller support domain is used; the strain calculation accuracy is slightly lower than that of the MLS method by 0.3–0.5%, but the stability of the calculation is significantly improved. In contrast with the PLS method, which is easily affected by the noise and the size of the strain calculation window, the RPIM is insensitive to the displacement noise and the size of the support domain and can obtain a similar calculation accuracy. The RPIM with multiquadric (MQ) radial basis functions performs well in balancing the computational accuracy and efficiency and is insensitive to shape parameters. The application cases show that the method can effectively compute the strain field at the crack tip, validating its applicability to the study of the plastic region at the crack tip. In conclusion, the proposed RPIM-based method provides an accurate, practical, and robust approach for full-field strain measurements.

Tensile characteristics of ultra-high-performance fibre-reinforced concrete with and without longitudinal steel rebars

Ultra-high-performance fibre-reinforced concrete (UHPFRC) specimens with and without longitudinal reinforcement were experimentally tested under direct tensile loading. The variables considered were the volume fraction of fibres (1.0% and 2.0%), the type of steel fibres (straight and hooked end) and the longitudinal steel reinforcement ratio (none and 1.2%). All of the specimens were tested using a servo-controlled fatigue testing machine in displacement control mode. The changes in displacement were monitored using linear variable displacement transducers and the digital image correlation technique. The strain profiles at different loading stages were used to identify the crack evolution process. The average localised strain was found to be 0.2–0.36%, with corresponding crack widths of 0.3–0.6 mm. A uniaxial tensile stress–strain model was developed based on the test results and data from the literature. Longitudinally steel-reinforced specimens showed both stiffening and strengthening effects. Tension-stiffened specimens with 1.0% fibres failed at a higher strain due to the formation of multiple macrocracks. In the specimens with 2.0% fibres, the rebar fractured in a brittle manner due to crack localisation. It is concluded that a higher longitudinal reinforcement ratio is needed to utilise UHPFRC effectively under tension-dominant loads.

CD163+ macrophages in the triple-negative breast tumor microenvironment are associated with improved survival in the Women’s Circle of Health Study and the Women’s Circle of Health Follow-Up Study

BackgroundTumor-associated macrophages (TAMs) are a prominent immune subpopulation in the tumor microenvironment that could potentially serve as therapeutic targets for breast cancer. Thus, it is important to characterize this cell population across different tumor subtypes including patterns of association with demographic and prognostic factors, and breast cancer outcomes.MethodsWe investigated CD163+ macrophages in relation to clinicopathologic variables and breast cancer outcomes in the Women’s Circle of Health Study and Women’s Circle of Health Follow-up Study populations of predominantly Black women with breast cancer. We evaluated 611 invasive breast tumor samples (507 from Black women, 104 from White women) with immunohistochemical staining of tissue microarray slides followed by digital image analysis. Multivariable Cox proportional hazards models were used to estimate hazard ratios for overall survival (OS) and breast cancer-specific survival (BCSS) for 546 cases with available survival data (median follow-up time 9.68 years (IQR: 7.43–12.33).ResultsWomen with triple-negative breast cancer showed significantly improved OS in relation to increased levels of tumor-infiltrating CD163+ macrophages in age-adjusted (Q3 vs. Q1: HR = 0.36; 95% CI 0.16–0.83) and fully adjusted models (Q3 vs. Q1: HR = 0.30; 95% CI 0.12–0.73). A similar, but non-statistically significant, association was observed for BCSS. Macrophage infiltration in luminal and HER2+ tumors was not associated with OS or BCSS. In a multivariate regression model that adjusted for age, subtype, grade, and tumor size, there was no significant difference in CD163+ macrophage density between Black and White women (RR = 0.88; 95% CI 0.71–1.10).ConclusionsIn contrast to previous studies, we observed that higher densities of CD163+ macrophages are independently associated with improved OS and BCSS in women with invasive triple-negative breast cancer.Trial registrationNot applicable.

A comparison among a fuzzy algorithm for image rescaling with other methods of digital image processing

The aim of this paper is to compare the fuzzy-type algorithm for image rescaling introduced by Jurio et al., 2011, quoted in the list of references, with some other existing algorithms such as the classical bicubic algorithm and the sampling Kantorovich (SK) one. Note that the SK algorithm is a recent tool for image rescaling and enhancement that has been revealed to be useful in several applications to real world problems, while the bicubic algorithm is widely known in the literature. A comparison among the abovementioned algorithms (all implemented in the MatLab programming language) was performed in terms of suitable similarity indices such as the Peak-Signal-to-Noise-Ratio (PSNR) and the likelihood index $S$.

Residual Stress Determination with the Hole-Drilling Method on FDM 3D-Printed Precurved Specimen through Digital Image Correlation

The hole-drilling method (HDM) is a common technique used for the determination of residual stresses, especially for metal alloy components, though also for polymers. This technique is usually implemented with strain gages, though other methods for determining the fields of displacements are quite mature, such as the use of digital image correlation (DIC). In the present paper, this combined methodology is applied to a 3D-printed PLA precurved specimen that is flattened in order to impose a bending distribution which can be considered known with a reasonable accuracy. The back-calculated stress distribution is in agreement with the expected (imposed) bending stress, however, a converging iterative procedure for obtaining the solution is introduced and discussed in the paper.

Automated system for the detection of risk in agricultural sugarcane harvesting using digital image processing and deep learning

In recent years, significant changes have been presented in the climatological trends due to climatic change, originating negative impacts on the agricultural production, diminishing mainly the harvest efficiency. The following research proposes the optimization of the agricultural risk identification method for the prediction of the variables: temperature and precipitation; the risk identification method was developed through the Digital Image Processing technique (DIP) and Deep Learning (DL); Subsequently, with the processed images, Convolutional Neural Networks (CNN's) were developed for the detection of areas where there is a potential risk in the sugar cane crop harvest in the southeast of Veracruz in Mexico. The efficiency of CNN detects temperatures over 38ºC and the levels of precipitation under 70 millimeters. The efficiency of network detection is 0.9716 and 0.9948 for predicting the temperatures and precipitation variables, which represent a solid basis for detecting zones that depict a risk for the sugarcane harvest.

Content Based Image Retrieval Using Support Vector Machine

A significant issue in multimedia systems is image retrieval. It is identified as the procedure for looking through and obtaining pictures from a dataset. One important and difficult area of study in digital image processing is content-based image retrieval (CBIR). Retrieving pertinent data from a vast picture database after a query image with better system output is a fundamental requirement of the CBIR system. Regretfully, not all techniques can be applied to achieve high retrieval accuracy. Thus, the purpose of this research is to use Support Vector Machine (SVM) to classify query picture data with image database data in order to get similar images. Based on the classification, the accuracy of the image retrieval will be verified.

Interferometric-scale full-field vibration measurement by a combination of digital image correlation and laser vibrometer

Interferometric-scale full-field vibration measurement by a combination of digital image correlation and laser vibrometer

Autoencoder-based Image Denoiser Suitable for Image of Numbers with High Potential for Hardware Implementation

The algorithms for processing images and videos are currently essential for many applications. Many of these applications are specified for processing and analyzing images of numbers, such as smart meter reading, automated document processing, and processing of vehicles and license plate images in traffic monitoring and analysis. Consequently, eliminating noise is frequently used as a pre-processing step to improve subsequent analysis and processing outcomes. In this context, this manuscript proposes using artificial intelligence-based methods to increase the efficiency of the image-denoising process. However, the computational demands of these algorithms necessitate careful consideration of the hardware on which they are implemented. Therefore, this paper proposes using the simple autoencoder approach and evaluates its efficiency compared to the conventional methods. This unsupervised model is trained to identify and remove impulse noise from digital images by replacing some pixels with others from the outer dataset that can clarify the whole image more. The model was trained using handwritten numbers, MNIST, and data set size in the first trial and the FER2013 dataset in the second. The model is superior in the case of the simple dataset. Two versions of autoencoders are considered, the first with three layers and the second with five. The Traditional denoising methods are investigated for comparison purposes. The four conventional filtering procedures, AMF, DBMF, ADBMF, and MDBUTMF, are implemented using the MATLAB simulation environment, and the results are reported and compared with the proposed methodology. The results show that the proposed artificial intelligence-based method significantly outperforms the traditional methods regarding processing efficiency and the resulting image quality. Moreover, the computational intensity for the proposed methodology is chosen as a metric for evaluating the algorithm compliance for the hardware implementation compared to the other Artificial Intelligence (AI)-based denoising algorithms. The suggested technique has minor processing and training time compared to the other AI-based methods with adequate quality in case the images of numbers usually do not contain many details, making it more convenient for hardware implementation.

Dynamic mechanical properties and failure mechanisms of rock-like materials containing main and auxiliary holes with various arrangement inclinations and spacing ratios

As a typical rock defect, circular holes significantly influence rock masses, diversifying their failure modes and ultimately weakening their bearing capacities. To study the effects of hole arrangement inclination and spacing ratio on the mechanical properties and failure mechanisms of rock-like specimens, dynamic uniaxial compressive tests were conducted by employing a modified split Hopkinson bar apparatus and digital image correlation. The results reveal a V-shaped trend in the peak stress, Young’s modulus, and absorbed energy proportion as the hole arrangement inclination increases from 0° to 90°. However, the peak strain is insensitive to changes in the hole arrangement inclination and spacing ratio. The hole spacing ratio has no significant effect on the Young’s modulus, but it causes a gradual reduction in the proportion of energy absorbed in the specimens. Moreover, shear and tensile cracks emanating from the main hole dominate the failure mode of specimens with main and auxiliary holes, as demonstrated by the resultant displacement and vertical strain contours. Rock bridges experience shear failure as the hole arrangement inclination increases from 15° to 75°, whereas the rock bridges of specimens with various hole spacing ratios tend to fail owing to tensile cracks. The interaction between main and auxiliary holes with an arrangement inclination of 0° is the weakest, suggesting that tunnel groups should be arranged perpendicularly to the direction of dynamic loading.

Flexural behaviour of one-part fibre-reinforced geopolymer beams

This research focuses on the structural application of one-part fibre-reinforced geopolymer composite (OP-FRGC), a pioneering low-carbon, high-performance construction material. While extensive studies have been conducted on the basic mechanical properties and mix proportions of OP-FRGC, structural applications remain underexplored. This investigation of the flexural performance of steel-reinforced OP-FRGC beams employed four-point bending tests and digital image correlation (DIC) to elucidate bending failure mechanisms. The results were then compared with those of conventional steel-reinforced concrete (RC) beams. Findings indicate that the integration of fibres in OP-FRGC effectively distributes cracks and enhances flexural toughness. Additionally, the research evaluates the predictive accuracy of existing structural codes from China, the United States, and Europe for steel-reinforced OP-FRGC beams. A comparative analysis of the carbon emissions from these structural systems underscores the environmental advantages of steel-reinforced OP-FRGC beams, highlighting their potential to reduce the carbon footprint in the construction sector.

Mechanical and fracture properties of mortars reinforced with glass fibre and prepared with different cement types

PurposeThe main aim of the current study is to investigate the effect of Anti-Crack HP 67/36 glass fibre on the mechanical performance of mortars made of cement, with a focus on post-cracking evaluations using the digital image correlation (DIC) technique.Design/methodology/approachExperimental tests were carried out on 36-mm long fibres at 0.8% by volume and added to the normal strength (NSM), high strength (HSM) and high strength mortar with fly ash (HSMFA) mortars. CEM I 52.5 CP2 NF, CEM II/A-L 42.5 NF and CEM III/C 32.5 N-SR PM were used for each series of mortar to assess the performance of the glass fibres with the types of cement. F-class fly (FA) ash was used to reduce global CO2 emissions.FindingsThe mortar’s strength decreased as the cement types changed from CEM I to CEM II and III. However, due to changes in the portlandite content of the cement, water porosity increased for both types of mortar, without and with fibre. It was also found that using glass fibre increased flexural strength more than compressive strength, regardless of the type of cement used. For all the strength classes, it was found that the mortar mixes with CEM I had the highest critical crack opening (wc) and fracture energy (GF), followed by CEM II and III. No significant effects were observed in the mortar’s property by replacing fly ash (12%).Research limitations/implicationsOnly mortars were formulated in this study, but the results must be verified at the concrete scale.Practical implicationsValidation of the DIC technique to characterize the post-cracking behaviour of cement-based material. Use of glass fibres to improve the material’s resistance to cracking.Social implicationsUse of CEM II and CEM III cements with low CO2 footprint instead of CEMI without altering the mechanical performance of the material.Originality/valueThe work is a further contribution to studying the cracking behaviour of several series of variable mortars depending on the resistance class and the type of cement used.